More than the sum of its parts -- splitting requirements

In the last article on the right size of a user story, I discussed some aspects that contribute to finding an answer that works in your context: clarity, functionality and effort. In this post, I want to discuss the question on how you can move from a big feature to smaller stories. The assumption here is that we already understand the requirements “good enough” (i.e. maybe captured in a use case document), so we have reached already a quite clear picture of what needs to be done.

Špalek na štípání
By Chmee2 (Own work) [GFDL, CC-BY-SA-3.0 or (CC BY-SA 2.5-2.0-1.0 ], via Wikimedia Commons

There is quite bit of literature on these questions, with the book “Agile software requirements” from Dean Leffingwell being one particular source of input (the SAFe page on stories lists an overview). Being asked, what would you recommend, the usual consultant answer would be “it depends”, but I think it is safe to come up with some rules of thumbs.

0. Don’t split

The bottom line for story splitting is that you should avoid it if you can. Having a lot of very small stories will come at a price — loss of overview is an important one, and if taken too far, it can also become hard to reason about the business value of a story. Formulating the “… in order to” part of the classical user story template (“As a <role>, I want <action> in order to reach <my goal>”) is often a good sanity check: if you have a hard time finding the wording for “my goal” is usual a sign that you’ve split up the requirement too much. There is one classical mistake that can easily result in this situation: if you break down a requirement by components or systems, it’s highly likely you will end up with problems to formulate a reasonable business goal — most probably you split off an implementation task, cf. below.

Assuming the main reason is to fit the work into one iteration (or one week of the iteration, cf. above), if your story is already “small enough” in terms of effort, then there is not much point in going for even smaller stories. You will run into the occasional discussion that a story should ideally be able to finish (“move to done”) within one day, but it should be up to the team to decide whether pushing for this really brings any benefits like additional helpful feedback. More often than not, the team might just try to game numbers, e.g. the number of stories they can finish. There is an obvious tension here between the benefits of small stories (delivering working code early, enabling quick feedback) and unnecessary overhead (splitting in a meaningful way, documenting, managing and tracking the bigger amount of stories), that needs to be balanced according to the situation at hand, but try hard to avoid splitting into too tiny details.

The other question is, do you need to split the requirement now? If the requirement does not need to be elaborated now, because it is not planned for the current or the next iteration, there is no need to split it now — potentially this requirement could change over time anyway, so preliminarily driving for details might actually cause more problems than help. For instance, when a bigger feature involves multiple workflow steps and you split the feature into stories, where each implements one step at a time, you might have a hard time in your backlog tying these pieces / steps together, if you want to revamp these steps in its entirety.

1. Do the minimal thing first

If you think you need to split down a requirement, try to identify the “minimal viable functionality” as the first story. This is the minimum functionality that the system needs to provide that brings some (potentially rather minimal) benefit to the user. This functionality becomes the first story, whereas all other aspects of the requirements are addressed (as extensions) in separate stories.

Which parts provide the minimal usable functionality?
Foto assumed to be by Pavel Krok~commonswiki under [CC BY-SA 2.5], via [Wikimedia Commons]

As an example, let’s again think about user preferences for some system: the minimum useful functionality for a user consists probably of setting the preferences, saving, loading and applying them. Simple counting these steps leads you to the observation that this alone consists of at least four different functionalities. However, exporting my preferences to a file and importing them on a separate system is certainly not “minimal”. Depending on the intended users, even providing an inline editor to set the preferences might not be part of the minimum functionality. These functionalities could become separate user stories. The drawback of using this approach is that the resulting stories are not truly independent any more: you cannot prioritize exporting and importing preferences to be done before setting, saving and loading them.

Now, of course, this minimal viable functionality might still consist of a lot of work which might still be “too big” (in implementation effort, not fitting into one iteration). Here all the ideas from Dean Leffingwell are applicable, in particular trying to split by persona or business rule variations, data entry methods or data variations or workflow steps. In our example, setting preferences and saving as well as loading and applying preferences could be two different workflow steps (and hence different stories). However, this approach would probably result in stories that are too small according to rule of thumb 0 above. When splitting by workflow steps, try to have a minimal complete workflow in one story or at least plan to have this minimal workflow implemented in one iteration.

2. Optimize later

A good guiding principle in programming is to avoid premature optimization. While the principle talks mostly about optimizing performance of computations, we can also use it as a rule of thumbs for splitting requirements. The idea is to to take the simplest approach that could possibly work. This has a rather broad applicability in terms of technical aspects (architecture), but also usability and other so-called quality or non-functional requirements. After implementing this first rough version, use the feedback to check if it is “good enough” — e.g., whether performance, usability or security requirements are met already. If not, optimize these “-ilities” later on.

Some people might find this suggestion offensive. Security, for instance, is one quality that many claim cannot be designed into a system as an afterthought. But this is also not what I’m suggesting. Instead, I’m suggesting to formulate these “-ilities” requirements separately to isolate the functional core of the big requirement and to implement the optimizations later. This is quite independent of the need to design them — whether you design them upfront (as this might be part of your constraints in a “Definiton of ready”) or within a Sprint is a different question, but of course you need to think about the required qualities that the functionality needs to have. The idea here is simple: If your story is too big, put the proverbial lipstick on the pig in the next round. If you are afraid that your product owner will not care about security and will lower the priority of implementing these security stories to the point that they will never be implemented, your product has a bigger problem anyway (and probably security problems already) or the usage scenario that the product owner has in mind might be different from your expectation. Probably in both cases spelling out the required quality (and the accompanying work and effort) in a separate story could even be helpful to spawn a discussion.

That being said, breaking down your stories by functionality should be preferred. This is especially true if your stakeholders have some expectation of these qualities — if you miss out on these qualities, you’ll likely receive feedback to that effect. At least, you’ll be able to point to another story then to address it. Again, finding a good balance is important, but also not always easy.

3. Avoid splitting by technical components

Fat Slice pepperoni pizza slice
Who would prefer the naked pizza crust over a slice of the real thing?
By BrokenSphere (Own work) [GFDL or CC BY 3.0 ], via [Wikimedia Commons]

Technical user stories that are intended to cover work specific to some component are an anti-pattern. Typically they are brought up by people which are still stuck in the old trap to split work according to responsibility for features vs. components, cf. the discussion of features and components in SAFe. Don’t split by technical components unless the component can deliver business value / user functionality on its own.

Typically you cannot test some functionality in a reasonable way for some component on its own (e.g. the UI alone, not connected to the database). Hence, you will also not be able to get any feedback for the implementation of the story. E.g., saving and loading your preferences from the database is certainly a reasonable technical task, but how would your stakeholder test this without the UI being implemented? How would your product owner find out that there is a minor problem in the saved data if the only way to access it requires sending a web service request or proficiency in your programming language of choice? So, even though you might have finished the work on the component completely, you will not get the quick feedback that we would like to enable with small stories. And, of course, the component alone will also not be delivered as a result of your iteration, so it’s also not adding any business value at this point either.

This rule of thumb also can be debated. There are situations where one component can provide value already — e.g. a CMS system where you extend some functionality on the authoring environment first, so the editors can easily start using it. Even if the readers cannot yet see the result of the new features, some business value might already come from easing the work the editors. This case should not be confused with a different line of reasoning: For the UX designer, for instance, it might make a lot of sense to provide feedback on a UI preview that is not connected to the database. Your DB admin might already point out problems with the schema etc. However, this is inter-team feedback then, not customer feedback. In the end, the measure of progress in agile projects is delivering working software to the customer — and from a user perspective, both of these examples fall short, as essential functionality is missing. Instead, go back to rule 1 and try to identify the minimal complete slice of functionality that delivers value to the customer.

Task splitting and Spikes

There is, however, of course also a time when it is exactly the right thing to do to break down a story into technical tasks that need to be finished: this happens when the story is ready and due to be worked on given its priority, typically as part of a Sprint Planning 2 meeting in a Scrum setup. Especially for engineers it is easy to fall into the trap to confuse story splitting (as requirement work upfront) with task splitting (organization of concrete work within a sprint). A focus on technological considerations (and often also personal priorities) can quickly lead to splitting work such that it is developer-friendly, but does not deliver results to customers quickly. Let’s discuss some aspects of how task splitting influences work organization.

Stating the obvious, splitting technical work into tasks requires technical understanding, both of the system in which the requirement is implemented and of the technologies involved. But it is also not always the case that the team understands the system or the technology well enough. In this case, the team can decide to spend some time on research work to figure out what exactly needs to be done. Don’t define this as a “research implementation” task and plan to complete the entire story, you likely have no idea how much work it takes to complete everything anyway. Instead treat the research work separately, this is usually called a spike. As the goal of a spike is to learn something and not everything, a strict timebox should be defined (usually not exceeding one or two days), after which the spike is called of, regardless of how much insight was generated. Spikes usually are hence not estimated in story points, but reduce the capacity of the team, so you should also try to not have more than or two spikes per iteration.

Felling snags on fire line around the Coquille CCC camp, Siskiyou National Forest (3226072285)
Work in parallel to get it done
By OSU Special Collections & Archives, via [Wikimedia Commons}

Splitting a story into tasks follows different rules: for instance, splitting by components is usually a good idea, especially if your team does not consist only of full-stack developers. Also, even with full-stack developers, you need to start somewhere, which good software architecture practices would demand to be clarifying interfaces between the various components. Which brings us to one big issue of task splitting: Identifying dependencies between tasks. These are usually directly following from the organization of the components. Of course, as with user stories, you should strive to avoid dependencies in the first place (and use dependency injection and other mechanisms to decouple your dependencies). This is not only good engineering practice, but also allows to parallelize the resulting work (i.e. have multiple people work on the same story). Teams sometimes split their work such that only one developer is actively working on a story that consists of multiple tasks. This usually leads to the situation that many stories are started but they all take a long while to be completed. Instead try to distribute work between multiple people: the frontend ninja can start his JS foo, while the backend expert whacks the database in parallel, modifying the previously defined contract as required during the work. If the team can work on multiple tasks of a story in parallel, then this does not only encourage collaboration and team spirit, it will also ensure that the highest priority work items are finished first.

But the tasks to complete a story usually also involves other work besides coding, e.g. UX work or manual exploratory testing. More often than not, these are not happening as agile “on the spot” as other work, instead it’s a quite common anti-pattern to treat an iteration like a mini waterfall: UX (of everything) happens at the start, then development, testing (of everything) happens at the end. This can lead to a lot of unneccessary work done upfront (UX for stories which the team actually doesn’t get around to) or too late (testing starts so late it cannot be completed in the Sprint). Again, try to do the work only when it is necessary and try to do more stuff in parallel.

Requirement anorexia -- does size matter?

One of the recurrent puzzles with regard to requirements management that I keep running into is the question what would be the “right” size of a requirement. It often starts with some people coming up with very tiny and very specific requirements and then some very huge, completely fuzzy ones to go along with them. Then they have heared or read somewhere that in agile setups requirements should be small and wonder what this means and how to get there.

Cartoon by Geek&Poke on Epics
Advanced Scrum
Cartoon by Oliver Widder, licensed under CC BY 3.0 from [Geek&Poke](

These huge requirements are often called “epics”: An epic is a requirement that is either felt to be too “large” or is too “fuzzy” and one way to avoid never-ending dramas is to split these into tinier user stories. Actually, here we already have two very distinct aspects: a requirement engineering question of how to move from unclear feature ideas to concrete detailed requirements on the one hand and a requirement management question on how to organize requirements such that they are appropriately prepared for implementation. I discussed the requirement engineering part in an article on agile conception already, so I’ll focus on the latter part here. This size question is actually two-fold: What is a reasonable small story? And how can we break down a big feature into several smaller stories? I’ll address the first one below, the next one in an upcoming article on splitting requirements.

Why small stories?

Let’s first explore the reasons to go for smaller stories. As mentioned above, there is often a felt need to come up with “small” user stories, whatever “small” might mean. This is often even part of making a requirement “ready” for the team to start working on it (cf. also how a felt need for “small” stories is a hint for immature teams).

The main reason behind moving from “big” feature requirements to “small” user stories typically boils down to the team wanting to ensure that the work they take on can be finished within one iteration. From the agile point of view, that’s a good idea: We want to deliver value in terms of working software as quickly as possible. But even if your delivery is not exactly continuously and also doesn’t always happen at the end of the iteration, there is a lot of value in using small stories: developers, UX, product owner, stakeholders should give and take feedback on the completed work to ensure the development is on the right track (in terms of “Did we make it right?” and validation “Did we make the right thing?”). The earlier we learn about problems, the better, so we can make easy adjustments. Arguably small stories can be implemented more quickly and thereby help getting the necessary feedbacks quickly. Also, in case adjustments need to be made, changing a small story is hopefully easy (instead of changing a big requirement). Small user stories also help planning: for one thing, it is usually far easier to come up with an accurate and precise estimate if you’ve broken down a big problem (“epic”) into tiny pieces.

What is a reasonable small story?

This one has an easy answer: any requirement that the team feels can be surely finished within one sprint is reasonably small.

Does size equal effort?

Unfortunately, this answer isn’t particularly useful. It is simply taking the outlined main reason for small stories as a minimal acceptance criteria. If we take a step back, the size of requirement seems here to be directly bound to the effort it takes to implement it. It looks like a continuum: from “no implementation effort” to “takes the entire team the entire sprint to implement”.

But a story that takes the team the entire sprint to implement is almost certainly still too big. Think about it from a risk perspective: let’s assume your estimate is just a bit wrong (as in “too low”), e.g. you discover some previously unknown technical problem during the implementation. In this case you will not be able to finish the story within the sprint — from a work management perspective, you clearly want to avoid this situation, as you’ll run into all sorts of unwanted discussions (from relevant ones like when you get feedback to irrelevant management ones like what can be done to help the team deliver on time or to come up with better estimates).

Burn up charts
Effect of story size on burn up chart

Some teams take a very harsh stance at this and demand a “ready” story should not take longer than one day (of implementation and testing). I don’t believe in such strict rules but think a good upper bound is “no longer than one week”. With story point estimation this recommendation might look strange, but the simple idea is that if you know your team delivers ca. 20 points per two-weeks iteration, a 13 point story is probably too big, whereas an 8 point story could be small enough.

Of course, you can use the same reasoning of risk and project management to demand even smaller stories: e.g., with a 20 point velocity of a team, and three stories with 8 story points each, how would you plan your next sprint? Commit to two stories (summing up to 16 points) and leave one as a stretch goal? If you start working on the stretch goal but are not able to finish it, your efforts and results will not be visible (for the stretch goal). Instead, if you could break down the stretch goal into a 3 and a 5 point story, you might be able to commit to more points. Also, your burn-up chart might look a lot smoother with smaller stories than with big stories, cf. figure 2. But beware: what we really want is quick feedback and delivery of valuable increments. If smaller stories help you with this, great, if not, you’re likely just gaming numbers or making management happy (cf. also Johanna Rothman on the value of burndown and burnup) .

Consider functionality, too

However, the size of a requirement could also be addressed differently. Boris Gloger (e.g. in his German book on agile estimation) argues that estimates should not be based on effort or risk but on functionality. From this perspective, the smallest reasonable story is one whose implementation will enable one functionality.

This looks somewhat circular, as we have now moved from one fuzzy term (size) to another (functionality), in particular as in todays complex systems “one” functionality could be composed of a lot of different pieces playing together nicely. But requirement work is all about defining functionality in terms of specific, detailed user activities or system behaviors and as such is arguably easier to grasp and figure out than some number of story points.

It is usually the best idea to look at the functionality from the user’s point of view, because this will most closely resemble the business requirement anyway. But note that, without going further into architecture considerations, you might want to define functionality per system / component if you can define and observe the expected behavior per component. The “observe” part here is important as it is implemented, observable behavior that you can give feedback on. For the very same reason of getting feedback from humans, defining functionality from the user perspective is more preferable.

Now that we have discussed what the smallest story in terms of functionality could be, this might not be the most reasonable choice in your context. As a made-up example, consider the feature of where you want to save some document in some system. An additional feature could be to save a document under a different name (“save as”). From the implementation point of view, the difference in functionalities might be so small that it’s very easy to implement both in one go, so the team might decide that splitting off the “save as” is not worth it.

Another example could be where one user visible functionality (e.g. sending an order) requires the interaction of two systems (e.g. sending the order from some web application to some ERP system). Considering just one system part (e.g. the web application sending) as a user story might not be reasonable — it cannot be tested alone, hence doesn’t allow for feedback and also doesn’t provide any business value without the other system part. I believe this is where the entire team needs to give its input and consider not only the system functionalities but also (all) the tasks and the what is the minimum that makes a product increment valuable.

So now we have two different boundaries: whereas size in terms of functionality provides us with a lower boundary for a requirement (“implement at least one user-visible functionality”), size in terms of development effort provides an upper boundary (“can be implemented by the team in at most one week of an iteration”).

Clarity is required

Now, I believe there is a third “size” of a requirement which is related to clarity or level of understanding. Recall that at the start of many projects (or product phases) you’ll have a lot of very tiny and detailed requirements and some “bigger” requirements which have not been explored (or engineered) yet (which is what I would call “feature ideas”). An example of the former would be a user story along the lines of “As an editor, I need a save-as button right next to the save button so that I can give a new name under which to save my document”, whereas the latter would be something along the lines of “As a user, I want to save my preferences”. Quite obviously, the former is quite “small” and “clear”: it already says that a button is needed and what the expected behavior is. “User preferences” instead is just a label which might contain a huge list of things that need to be made customizable by the user and unless we break down this list into details, this requirement needs to be considered “big”. Another way of looking at this “big” story is that it is certainly not “ready” for implementation, so it is surely not a reasonable small-enough story. But even for the much clearer “save-as” story, there might be some lack of clarity on how to give the filename or other aspects (e.g. technical issues). Again, obviously we have a continuum between very clear and completely unclear.

Unfortunately, clarity alone will not be enough to decide whether a requirement is “small” enough: you can get very clear requirements that consist of lots of functionalities and are way too big to be implemented within one iteration (never mind huge use case or detailed specifications). However, from the perspective of “can we start implementing this requirement”, clarity of what needs to be implemented is the key ingredient that distinguishes a requirement from being “ready” from others. Clarity is a prerequisite to breaking it down into different functionalities and come up with a reasonable estimate (although it’s often also true that trying to break a big unclear requirement into smaller bits and pieces to come up with good estimates will help getting more clarity). Turning this around, even if you have a story that is reasonably small in terms of effort, if the team is raising concerns about the certainty of the estimates, the team is probably not sure whether it understands the functionalities that needs to be implemented and hence the story is probably still not “ready for implementation”.

Summing up, this leaves us with three important aspects influencing the “right” size of a story: clarity, functionality and effort. In the next article, I’ll explore some rules of thumb to split a big requirement into smaller parts.

Technical debt equals missed quality requirements

Technical debt assumes that you have an existing system and you know already about the areas where the duct tape is becoming thin. Let’s discuss the problem, the conflict around technical debt a little. It’s basically always the same battle: the guys maintaining the system probably have good reasons why they want to pay back on the accumulated technical debt, whereas the product owner believes more functionality is more important.

This is a typical clash of interests between people from different tribes and usually from different departments: the developers report to a technical manager, the product owner to a business guy. The IT manager is typically under pressure to minimize costs, in particular costs for support and maintenance, so he’s interested in paying back on technical debt during projects. The business manager, however, is under pressure to convince new clients with new features. So, that there is a difference in how technical and non-technical people set priorities between technical debt and new features doesn’t come as a surprise: naturally, to the business side this is all about technical issues which they don’t consider to be their concern. It’s only natural that they believe that tackling such technical issues should be solved by the technical folks “somehow”. After all, it were these guys which build the system with all these problems in the first place, and now they want to spend more time and money on this? Although I’ve exaggerated here a little maybe and this line of argument is way too simplistic (given that often debt is accumulated over time), it’s still quite popular. Such product owners seem to believe that they are only responsible for developing new functionality. Corporate culture can contribute to this: some companies have a culture where every new glitter is taken for a star and gets way more attention than the cash cow functionality that keeps existing clients happy.

So some product owners will see technical debt as a separate issue which needs to go on a maintenance budget, a budget that somebody else is responsible for, e.g. the IT department. The problem with that idea is that in a culture where building new features is the only thing that counts, typically no one ever gets around to clean up the technical debt. All what will happen is that you’ll have some people that fix bugs with this maintenance budget. So you don’t actually ever spend the money on technical debt but only on the results of it which of course doesn’t address the root of the technical issues. Also, if you finally do get an approval for a refactoring project, these are really the most boring and horrible types of projects to work on. So it’s not likely to see a lot of happy, motivated people on such projects — and sure enough the most competent people are probably assigned to more valuable projects anyway. As a result and because there is no business pressure on such refactoring projects, as soon as something else comes up, such refactoring initiatives are abandoned or at least down-sized to a minimum. Peter Seibel describes in a recent, lovely written article the need for an Engineering Effectiveness team at Twitter in which he lays out in some details how hard it is to really put up with technical debt.

Let’s drill down on technical debt from the point of view of requirements. Wait a minute, this doesn’t sound right — how could technical debt be a requirement? Obviously, it isn’t, but there is a close relation. Technical debt can come in different flavors: e.g. you really should install a second machine and a load balancer so you’re prepared for failure or we really need to rewrite module foobar.clx to finally get rid of all the spaghetti code that’s slowing us down to oh, we really need to support responsive design, we’re getting more mobile users every day. Now, if you take a look at these three simple examples, they are related to quality requirements: the first is about availability, the second about maintainability and the last about usability. Take a look at the ISO 25000 standard for software product quality page listing all the quality attributes one might want to consider. Technical debt is always tied to some quality that your system should offer, but doesn’t.

A reason for this is that quality attributes are often not made explicit during requirements gathering, regardless of whether you’re following an agile approach or not. The less technical the product owner, the more often they seem to assume that performance, scalability, security and the other -ilities will come out just right by magic. Technicians, on the other hand, know perfectly well that they will not. It’s worse if they don’t that: they will build something that might fulfill the functional requirements but not the non-functional requirements. “Works on my machine” might be fine for a naive developer, but nobody will be happy if takes 5 minutes to load the page for the gazillions of parallel requests in production. There is a reason why software architecture is mostly concerned with quality, if you don’t plan and build for scalability, it’s unlikely you end up with a highly performing system. Take a look at the picture: this bridge wasn’t planned to cope with the amount of traffic it sees nowadays, some time in the 80s somebody decided it would be okay to have three lanes instead of two and didn’t think through the long term consequences.

Rheinbrücke A40 Duisburg-Neuenkamp
The bridge of the highway A40 over the Rhein in Duisburg. It is damaged so bad that trucks are no longer allowed to cross it.
Picture made by kaʁstn Disk/Catstitched by Daniel Schwen, licensed under CC BY-SA 3.0 de over [Wikimedia Commons](

Of course, technical debt can also accumulate over time, one might rightly point out: sure, one machine was enough for the requirements at the start, but nobody followed up on the increase in users and nobody cared (or had time) to clean up the code in module foobar.clx back then (nor in the following six years of quick bug fixes and minor one-off patches that have grown on it like leeches). This is a sign that nobody actively had an eye on how the world in and around the systems changes and to take action early on. For code quality, Uncle Bob points to the Boy Scout rule which says that you should always clean up (regardless of who messed it up) — another way of trying to ease maintainability by paying back on technical debt every day.

The over-aching point here is that you, as a technician and you, too, as a product owner need to think about the quality requirements of your system and to do that over the entire life cycle. What was a fitting solution at a time might turn into technical debt over time. This means means your system now doesn’t hold up to the quality requirements you and your clients needs and no matter what was the cause for it, you’re better of fixing it now than accumulate even more technical debt.

Isaac Sacolick describes in his article on How to get an agile product owner to pay for technical debt how to address the common problem of technical debt heads on, mostly from a managers perspective via process and by making people (mostly the technical lead) responsible. However, I think it makes a lot more sense to try to ensure a common understanding between the developers and the product owner. One way to go about this is to use quality scenarios: as a member of the dev team, ask your product owner how many users the system needs to handle. Ask her if she thinks it’s okay if somebody might find a security issue earlier than you. Or if she thinks it’s okay when a seemingly small change will take three weeks because the code is an intangible mess that nobody understands since Dieter left? These sort of questions hopefully open up to discussions based on business value, ROI and cost of delay. You are then using the language product owners understand and you might also learn something along the line (no we expect so many users to do X in the future, so investing in module Z doesn’t make sense).

Now, granted that doesn’t always work. An old colleague of mine always denounced the old system he worked on as being exactly the way it was ordered. Isaac’s article might offer some advice here, of which the most important one is probably that paying back technical debt is way easier if you have a CIO or someone with similar power supporting you.

Tiny cities made of ashes -- stories of agile maturity

Tom Boulet has recently triggered a discussion in the Agile+ community on Google+ about user stories, cf. the following quote from his inquiry:

A couple weeks ago, I heard someone say “The stories weren’t detailed enough for the development team..” I thought, Wha? Stories are meant to be high level and if you use acceptance criteria this comes after the team starts looking at them, doesn’t it? It sounded like the person felt that Stories had to be “fleshed out” somehow before development could get started. […] Also, I’ve recently seen talk of how stories should be small enough to code in a day or two (I’d call that a task) and another reference to “codable stories.” None of this sounds right to me. It sounds like a large number of Agilists are seeing stories as part of the design pre-development. Has the definition of Story shifted on me?

The resulting discussion is highly insightful. I particularly like one reply by Alan Dayley, which I quote in some parts:

It is hard to describe in brief sentences all the forces that pull organizations to keep them thinking in lots of details. A few might be: - A culture and history of Big Design Up Front that makes it hard for feature definition to happen just in time. - A continued need for long range planning requiring estimates for work that will possibly take months before it actually starts. - Skilled technical people who are accustomed to talking about technical details instead of the needs of the user. And more…

The most powerful and subtle force to cause “user stories” to grow is a continued lack of focus for the people defining features and those creating the features.

For example, if I am Product Owner of 5-12 products working with 3 teams who are all working on multiple products, I am not able to think of a feature, collaborate on it’s definition as it is built, see it created and then think about the next feature. I have to document my thinking of every feature because one hour from now I have to think about other features that also might not be built right away.

The key to being truly Agile is to finish stuff. The more inventory of ideas, features, undeployed code, etc. that we have, the less Agile we can be.

This highly resonates with my thinking. But it also got me thinking and reminded me of another discussion I participated in in one of Xing’s agile groups: for many companies, being able to keep stories high-level, have the Product Owner engage with the developement team just in time and clarify all those nitty-gritty details in short-enough time that the team can implement it, sound like the wet dream of agile heaven. I, too, have mostly seen teams in which there was substantial time invested by the Product Owner (at least), often accompanied by the Scrum Master and some development team members, to “groom the backlog” and to prepare the stories for the next sprint. This preparation, which more often than not involves adding lots of details according to some definition of ready, typically also includes story splitting. I’ve seen development teams straight out refuse to work on stories “too big” according to some arbitrarily set size limit. I guess the reasons behind this basically boil down to fighting FUD (fear, uncertainty and doubt), cf. this article listing potential benefits of small stories:

There are lots of benefits from having smaller stories.

From a user and usage perspective, reducing the story size forces me to understand users and usage better. It forces me to separate what really is valuable or useful to the users from what’s merely interesting. From a release management perspective having lots of smaller stories allows us more flexibility with planning, prioritizing, and cutting scope. From a development and detailed design perspective, a smaller story often implies we know more about what to build - there’s less unknown, less risk – less risk of the development work mushrooming into something much larger.

There are two obvious problems with this:

  • The development and release management perspective and their respective goals are not a good match for the business value that is driving a story. As a result of this, when you decompose stories into slizes too thin, the business value proposition, i.e. the “in order to ” part of Mike Cohn’s classical user story template, often becomes very awkward and only understandable in the direct context of some user interaction. Quick, tell me, what is the real business value of “As an order maker, I need to enter my credentials to access my orders” (cf. Godjo Adzic’s rant that writing “As a user” doesn’t make it a user story)?

  • The other problem is that often “understand users and usage better” is actually design work, as also asked by Tom Boulet in the discussion: “Can requirements really be separated from design? As requirements become more detailed don’t they really become design? Does this constrain the design part of development? […] And ultimately, can good design really be done without coding trial and error? Does a customer and PO really know what they want up front?” Here, the article by Mary Poppendieck on the Product Owner problem is helpful: according to her, the most important role of the development team is product design:

    The entire team needs to be part of the design decision process. Team members should have the level of knowledge of the problems and opportunities being addressed necessary for them to contribute their unique perspective to the product design. Only when decisions cannot be made by the development team would they be resolved by a product leader. The main team-facing responsibility of the product leader is to ensure the people doing the detailed design have a clear understanding of the overall product direction.

It would seem a development team with a “definition of ready” that requires lots of details and splitting stories into thin slizes is lacking this “clear understanding” and arguably refusing to take responsibility for designing the product. Again, my gut feeling is that this is a sign of the maturity of an organization, wrt. to agility: e.g., building this clear understanding typically requires a longevity of development teams which often clashes with the “project-based” approach taken by many organizations. And if development teams don’t know the market, the customers they are building the product for, where should this understanding come from?

In the discussion, Heiko Stapf also referred to the agile principle of “individuals and interactions over processes and tools” in the discussion, noting that “Having PO/Tester/Techguys sitting together finding a common understanding and language” is more akin to favor individuals and interactions, whereas “having the PO writing down requirements and passing them (in written form)” is more about process/tool. I find this to be indeed the case, where sometimes the underlying cause is that it’s difficult to arrange for enough room (time) to have the necessary interaction. But it might also be simply lack of collaboration, i.e. when developers just feel it’s not their job to work on (the formulation and details of) user stories.

I’ve also seen teams run into problems with getting the right information in time. IMHO they are either due to lack of knowledge on the Product Owner’s side or lack of empowerment. In the former case, when some detail needs to be clarified, the Product Owner might not have the necessary domain expertise required to answer the question right away and she needs to go back to some domain expert (which might not be immediately available, delays occur etc.). The later case is where the Product Owner has an opinion on some matter (e.g., whether to use this or that design) but feels she needs to go back to someone major stakeholder (potentially multiple). Also, when the team (PO+dev team) haven’t yet found the path to close collaboration, the resolution of some issue might require too much time to allow for implementation. In such scenarios, the idea of using a definition of ready with small stories is actually aimed at ensuring that some work can actually be finished in some time box at all (this is linked to ensuring a story is “feasible”, cf. Roman Pichler’s take on the “definition of ready”.

Of course, there are also legitimate reasons to split stories which are linked to other organizational issues: e.g., when the story is big it can’t possibly implemented by a single team in given small time box and hence needs to be split over multiple teams or multiple time boxes. Or when the requirement is yet more an epic than a “ready” story and when split, business value can still still be obvious for the smaller stories. And then there is also the process where you start out with a vague “feature idea” and need to understand it more before it would be ready for implementation, as I’ve described in my post on agile conception.

In summary, I would say that a large backlog with lots of tiny stories is a clear indicator of a lack of maturity of the team and / or organization. In an ideal world, all our teams would be mature enough that nobody would ever want to have too many detailed written specifications upfront, but alas in our reality we have to work hard to get there. Unfortunately, there is no easy answer to this than to try and persist: gain the knowledge and the confidence, build the tools and the trust you need.

ObTitle: Modest Mouse, from “The Moon & Antarctica”

Moving targets -- on agile conception

Two things are clear for every project and every product: there is always a start and an end. I’ll ignore the end for now and would like to take a closer look at the start. One has to wonder: where do all these projects or new products come from? Obviously, somebody finds out at one point that there is a need for something and this something is the nucleus of the perspective development. If this idea requires significant effort, it’s typically necessary to convince the people responsible for the money that is worthwhile to pursue the idea. Let’s take a closer look at what this something actually is and how it changes and needs to change before the developers can finally go make it real.

To use a more descriptive tag, I will use the term feature idea for now, although we could well talk about ideas for a new product or product line. Feature ideas in a very early stage are rarely more than a single, simple statement or even just a few descriptive keywords like “I would like a button here where I can save my settings” or “remember me functionality”. Of course, whoever came up with the idea has typically second thoughts, more or less fuzzy but these are not communicated, neither discussed nor finalized.

A feature idea

Quite often these second thoughts are related to some existing systems or solutions, even if it’s just to describe what the respective solutions do not provide (note that these solutions might not be your own). Typically feature ideas are about some specific expected behavior of some system and basically boil down to what needs to be build. This is all good: feature ideas don’t need further elaboration or lengthy descriptions in an early state.

How do you proceed from here to the nitty-gritty details that finally need to be done? In traditional settings, to get from the idea for a larger feature to development you need to jump over the budget hurdle, which usually requires detailed analysis results, including business cases, return on investment calculations, and implementation plans. The latter consist of solution outline, milestones, resource plans and cost requirements. It is important to note that to come up with all this substantial efforts need to be invested already. The bigger the need for precise information, the bigger the required investment. And then at some point you hopefully get the okay to move ahead. But in case the idea is turned down, all this investment in detailed plans was just wasted. In agile setups, we would like to avoid that waste. Still, of course, the need for getting a decision on where the business wants to invest its money does not go away. But in order to do so, does the senior management really need all those details? They don’t. There is a need for accuracy, not for precision (cf. for a description of the difference). Asking for all the details is a sure sign for lack of trust, but I have never seen any senior management trying to verify that all those precise details were actually accurate, which probably explains why the demand for documents and spreadsheets is all there is. This is then typically combined with taking all plans as promises that need to be kept and not as means to enable successful investments (command-and-control vs. plan-to-replan).

What is surely needed boils down to just one thing: understanding the value of an investment. It’s often much easier to determine what you want or can invest than to precisely estimate upfront what you need to invest (cf. this discussion of the no estimates idea which shows the wonderful relevant Dilbert strip on the same issue). On top-level, investment is essentially tied to strategy: e.g. in the Scaled Agile Framework (SAFe, cf. Leffingwell et al.), investment themes determine the relation between budget (resources) and the program portfolio of business epics. This essentially requires an understanding how each possible business epic or feature idea contributes to the overall company strategy (this is basically applying the Lean principle of “optimize the whole” to feature ideas).

We essentially danced around the big elephant in the room now: feature ideas often describe expected behavior, but we need to know about the value of an idea. But if all we have is a simple statement (and not the full-fledged analysis we would like to avoid) of some idea, how can we know its value? The essential insight is that it might not be necessary to understand the precise value yet. The earlier in the process we are, the less is it necessary to perform lengthy and detailed analysis: determining the value of some idea is only a means to further a decision on where to invest further. However, at least it should be possible to judge the value of one feature idea in relation or better said in comparison to other ideas, as Johanna Rothman discusses in her article on why cost is the wrong question for evaluation projects: at least in an early stage, it might be enough to use a very simple kind of rough value sizing, e.g. putting an “M” on one feature idea in comparison to a “L” for another. It is important to recognize that the value of ideas will change over time: in particular so when looked at in relation to other things your business needs to think about, as new ideas come up , some ideas get implemented and other discarded. Accounting for these changes over time is why planning to replan is important in agile methodologies. An important concept in agile methodologies that helps making this clear is the cost of delay: the idea is that you try to understand or predict how the value contribution of some feature will change over time, in particular what impact not pursuing the idea will have. This is most useful for prioritizing by cost of delay of what to invest in — obviously, as CoD is all about loss of value over time, as time moves on you want to reconsider your priorities (where you want to drill down further with an analysis etc.). (An alternative coming from the lean camp is to use an impact-effort matrix, but such a matrix needs an estimate about the effort required and also treats value as an absolute over time, which it usually isn’t. Cf. this article about cost of delay vs. WSJF in SAFe for a discussion why cost of delay and value considerations are not the same).

Inevitably, when you try to think about the value of some feature idea, you will think about what this simple statements like “remember me functionality” should actually mean. Very often the way in which we name or describe a potential feature is often a confusion (and at best a conflation) of problem and solution space and that can make it quite difficult to judge it’s value. As such, naive feature descriptions can quickly become a big impediment. For one thing, they are — due to being explicitly simple — usually ambiguous and / or often hard to understand. Does “remember functionality” describe that I stay logged in on the website or does it mean that the website remembers the visitor and shows specific ads related to his interests or both? This is often the reason why some people attribute completely different value to an idea. Worse, they typically don’t give any clue about the need behind the wanted behavior nor about the value this behavior brings. Just as bad is that simple statements of the feature idea in terms of expected behaviour (“I want a button that …”) limits creativity by already pointing in the direction of a possible solution: maybe there are other options which are much better suited, both from a usability point of view but also with regard to the required functionality. So the conflation of problem and solution is actually problematic for understanding the problem and for designing the solution.

So what is the process to move from a feature idea to the solution design? I think it’s useful to see feature idea statements as promises for analysis. The analysis of a feature idea should essentially focus on understanding the user and / or business needs the idea is related to. The stereotypical user story template “As a <type of persona>, I want to <perform X> in order <to achive Y>” is a useful tool in this respect as it is explicitly constructed to include and name the need (cf. Gojko Adzic rant Writing “As a user” does not make it a user story how this can go wrong when taken on too easy). Now you might not have such a story or might not have it yet and maybe it’s not clear what this simple feature idea is about. That’s the time to engage with whoever seems able to contribute, but first and foremost it should probably be the primary beneficiaries of the feature idea. Traditional requirements engineering practice would suggest to involve all relevant stakeholders (perspective users, management, marketing, legal department, …) and to employ whatever RE tool seems reasonable to use (e.g. brainstorming, workshops, open interviews, etc.). In agile setups, determining who actually is a stakeholder which needs to be heard is the responsibility of the product owner. I find that it is usually also a good idea to involve the team, because they will typically have a good understanding of the solution space and should also know about the envisioned user needs. Don’t take it too far, though: If you are still trying to analyze in order to understand the value of some idea, i.e. you are still at an early stage (inception phase as the disciplined agile delivery folks would have it) of your planned feature, you should probably try to keep it light-weight.

You will likely get a lot of input on your feature idea and many in the form of comparable simple statements to what you originally started out with. Understanding the needs requires going beyond the level of simple statements or simple suggestions of simple solutions (Laura Klein has a nice description of how difficult that can be: Sometimes users will tell you that they want a toaster in their car, when what they really mean is that they don’t have time to make breakfast in the morning.). And although the famous 5 Whys might come across somewhat strange or invasive in the course of an interview, it might take that many attempts to actually understand the problem that needs to be solved. If you’re lucky, a potential user will offer some simple insight as to the problem with simple statements such as “I would like a button here so I can save my settings, so I don’t have to adjust them again and again”. Further analysis might reveal that the real problem is that there is a usability problem and the user forgets to adjust the settings too often or that the user is a regular user which would like to spend less time with repetitive tasks. The main point here is that this process is about understanding the needs, which typically requires challenging any superficial and obvious ideas: this is both about maximizing the amount of work not done (simplicity) and about building solutions that satisfy the user.

Change in understanding over time
Change in understanding over time

This is now where the team basically takes over: the team is responsible for working out the solution, not only constructing / implementing it, but essentially designing the solution. This is not to say that the product owner or other stakeholders cannot or should not contribute ideas on how to solve it, quite to the contrary. It is quite essential to have stakeholders around to enable the knowledge transfer of needs from the users or stakeholders to the team. So while the separate paragraphs of this text might suggest a handover from product owner to development team, from needs to solutions, ideally this should be a smooth transition with lots of participation of the entire team and face-to-face communication. Tools like Roman Pichler’s product canvas are useful, but there is a reason why the agile manifesto recommends people interactions over tool usage. Again, for working out the solutions many ideas and tools can and should be exploited, from brainstorming over UI scribbles to models and prototypes. Typically when designing the solution, many alternatives come to mind and different people from different backgrounds will have different opinions and favorites. This is actually good, because you can embrace this diversity to think about the best possible solution. However, the best solution might not actually be the solution you want to implement, often the simplest thing that could possibly work is a reasonable way to move forward: this minimizes the effort required, thereby enabling faster time to market and thereby enabling earlier feedback on whether this solution actually works (this is the tension between trying to build the best thing vs. learning fast from feedback about what actually proves to work). People with lots of experience in designing solutions will typically also come up with all kind of challenging questions which might require drilling further down into the needs (e.g. questioning if the feature is targeted at the right people or what the legal department has to say to some feature). This will trigger more learning about the feature under consideration. But this should not be taken as an excuse to run endless analysis or design meetings: it is often a better approach to end a discussion with a decision and move ahead and find out later whether this decision was actually flawed and needs correction. It is often a good idea to consider the risks associated with some idea: the more risk, the closer you want to consider your options. In general, when in doubt about which option to chose, think about what data would be helpful to decide: performing light-weight experiments might be useful for minimizing these risks (e.g. technical spikes or usability tests with paper-pen sketches).

A brief terminology recap: an epic is just some requirement (feature idea) that is so “large” and / or uncertain that it needs to be broken down, i.e. nobody could take an epic and just implement it, the developers just have to come back and ask lots of questions. If you wanted to you could say after breaking down an epic, all you end up with are features but in agile circles these are usually split up into even smaller stories (cf. the image below). Of course, this is a completely arbitrary and often also highly context-dependent distinction. It’s also fairly common to use the user story template for describing epics. I used the term feature idea instead of epics to bring in some notion of time: epics start out as (feature or other) ideas. If you think about the timeline again, it’s clear that the closer you are to implementing some feature, the more details you need to know, i.e. the higher your level of understanding of the feature needs to be. And this is where the idea comes into play that you need to INVEST in your user stories, so that you can derive SMART tasks. This is obviously needed for the very next things you want to implement, and as any risk-aware tech guy or project lead will know, probably also a good idea for the next most things to do afterwards. Business cases are mainly needed when you are going to go for big investments and when it’s critical to understand the return on investment. But this brings us back to the discussion of value vs. cost and the discussion of precision vs. accuracy. Use cases, finally, are a useful tool when it comes to understanding some set of requirements in the context of a larger system. They can also be useful for driving discussions with stakeholders and are often a useful starting point if you like to model your problem / design before implementation. I see use cases as a useful tool on the feature level, to cover a broader range of more detailed stories. As such it does not matter much when to describe a use case: if it helps you drive your understanding of some problem, use it before drilling down to more fine-grained descriptions. If you already have a lot of details and you fear to lose the overview, it can also make sense to reconstruct some use case from more detailed stories.

Uncertainty vs. time
Uncertainty vs. time

Finally, let’s step back briefly and re-consider who is part of this solution team. It’s quite common that for feature ideas of different sizes, different people will make up the solution team. I.e. when your feature idea is concerned with some detail of some larger system, the team might just be the developer team (in the Scrum sense). For bigger features, projects or programs, it’s quite common that lead developers, design and usability specialists, solution and / or enterprise architects and maybe operational staff will play an important part in working out the solution. Whether these people are working in specialist teams (as e.g. SAFe assumes) or are part of the development teams is not of much importance, but having members of those teams working on the solution design is quite essential: if this is not the case, in the best case, the final developers will only have to rediscover and understand all the good reasons why you came up with the solution in the first place, in order to get it right. Worst, you might end up with frustrated developers which don’t take responsibility and don’t care about the build solution, because they feel they have no saying in its design.

On estimation

The need for estimates is a fact of (project management) life, whether you’re using a traditional approach or agile ones. The latter try to do away with any exact numbers and instead use anything from t-shirt sizes over gummi bears to story points and treat the idea of estimates as a tool that is mainly intended for the team. However, as to be seen in this article posted on the ScrumAlliance website on story points versus task hours the matter of concrete time estimates comes up every now and then — there are customers and managers that want it and you have to figure out how your estimate / velocity is related to the team’s capacity for any given sprint. TL;DR:

For me, story point is high-level estimation of complexity made before sprint planning.[…]The task-hour estimation, on the other hand, is a low-level estimation[…]should be done during sprint planning for highest possible accuracy.

But, just like every other project manager, I made the experience that there is no such thing as “highest possible accuracy”, regardless of the number of ‘points’ you’re estimating (three-point estimates, anybody? How does that fit in with agile methods?) What does somewhat “work” (for varying degrees of “somehat”) is measuring how much work is ‘roughly’ possible in an iteration (aka velocity in Scrum) and estimating the ‘entire’ efforts according to the law of big numbers, but this is exactly not task-hour estimation. I’ve also worked with task hour estimates during the sprint planning as well, but I wouldn’t rely on the results. Exchanging estimates between developers (aka planning poker) has the main effect of quickly leading to a common understanding of the tasks that need to be done, which in my experience has much more value than any estimate. The insight that numeric estimates are not all that useful (to say the least) is also discussed in this highly opionated article on stop using story points — this is not about going back to estimating in hours or days, but about teams reaching a level of competence, expertise and confidence in their respective context where estimates are just waste.

However, reaching this transcendental level might be hard to reach for many teams, due to lack of experience or because of other more context dependent reasins. Joachim Seibert discusses in his nice article for the “ObjectSpektrum” magazine (5/12) “Agile estimating in teams” (behind a paywall, unfortunately and only in German, sorry), that if you need to come up with the big number for budgeting, you should strive to look at the entire picture. I.e. estimating the overall effort is more important than trying to completely estimate everything down to the tiniest detail — which isn’t possible anyway. Seibert lists two key ingredients for that: estimating a reference story which you stick to throughout the project and comparing the relative complexity of requirements, essentially sorting them by ‘size’. This is easy to forget with planning poker, but is at the very heart of the two other techniques that Seibert discusses: estimation game (by Steve Bockman, apparently) and magic estimation (by Boris Gloger, AFAICT). Both focus on building a common understanding of the relative complexity of the features to be build and are much simpler to apply to lots of stories. If you add velocity to the picture, i.e. you actively measure how much you can build and you can reach a stable project setup (compare this nice sweet spot for agile methods by Philippe Kruchten), you might be able to end up with something useful from all the effort you put into estimation.

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