Cycle Time is a measure of how long it takes to get a new feature in a software system from idea to running in production. In Agile circles, we try to minimize cycle time. We do this by defining and implementing very small features and minimizing delays in the development process. Although the rough notion of cycle time, and the importance of reducing it, is common, there is a lot of variations on how cycle time is measured.

A key characteristic of agile software development is a shift from a Waterfall Process, where work is decomposed based on activity (analysis, coding, testing) to an Iterative Process where work is based on a subset of functionality (simple pricing, bulk discount, valued-customer discount). Doing this generates a feedback loop where we can learn from putting small features in front of users. This learning allows us to improve our development process and allows us to better understand where the software product can provide value for our customers. 1

This feedback is a core benefit of an iterative approach, and like most such feedback loops, the quicker I get the feedback, the happier I am. Thus agile folks put a lot of emphasis on how fast we can get a feature through the entire workflow and into production. The phrase cycle time is a measure of that.

But here we run into difficulties. When do we start and stop the clock on cycle time?

The stopping time is the easiest, most glibly it's when the feature is put into production and helping its users. But there are circumstances where this can get muddy. If a team is using a Canary Release, should it be when used by the first cohort, or only when released to the full population? Do we count only when the app store has approved its release, thus adding an unpredictable delay that's mostly outside the control of the development team?.

The start time has even more variations. A common marker is when a developer makes a first commit to that feature, but that ignores any time spent in preparatory analysis. Many people would go further back and say: “when the customer first has the idea for a feature”. This is all very well for a high priority feature, but how about something that isn't that urgent, and thus sits in a triage area for a few weeks before being ready to enter development. Do we start the clock when the team first places the feature on the card wall and we start to seriously work on it?

I also run into the phase lead time, sometimes instead of “cycle time”, but often together - where people make a distinction between the two, often based on a different start time. However there isn't any consistency between how people distinguish between them. So in general, I treat “lead time” as a synonym to “cycle time”, and if someone is using both, I make sure I understand how that individual is making the distinction.

The different bands of cycle time all have their advantages, and it's often handy to use different bands in the same situation, to highlight differences. In that situation, I'd use a distinguishing adjective (e.g. “first-commit cycle time” vs “idea cycle time”) to tell them apart. There's no generally accepted terms for such adjectives, but I think they are better than trying to create a distinction between “cycle time” and “lead time”.

What these questions tell us is that cycle time, while a useful concept, is inherently slippery. We should be wary of comparing cycle times between teams, unless we can be confident we have consistent notions of their stop and start times.

But despite this, thinking in terms of cycle time, and trying to minimize it, is a useful activity. It's usually worthwhile to build a value stream map that shows every step from idea to production, identifying the steps in the work flow, how much time is spent on them, and how much waiting between them. Understanding this flow of work allows us to find ways to reduce the cycle time. Two commonly effective interventions are to reduce the size of features and (counter-intuitively) increase Slack. Doing the work to understand flow to improve it is worthwhile because the faster we get ideas into production, the more rapidly we gain the benefits of the new features, and get the feedback to learn and improve our ways of working.

Improvements in communications technology have led an increasing number of teams that work in a Remote-First style, a trend that was boosted by the forced isolation of Covid-19 pandemic. But a team that operates remotely still benefits from face-to-face gatherings, and should do them every few months.

Remote-first teams have everyone in a separate location, communicating entirely by email, chat, video and other communication tools. It has definite benefits: people can be recruited to the team from all over the world, and we can involve people with care-giving responsibilities. Wasteful hours of frustrating commutes can be turned into productive or recuperative time.

But however capable folks may be at remote working, and however nifty modern collaboration tools become, there is still nothing like being in the same place with the other members of a team. Human interactions are always richer when they are face-to-face. Video calls too easily become transactional, with little time for the chitchat that builds a proper human relationship. Without those deeper bonds, misunderstandings fester into serious relationship difficulties, and teams can get tangled in situations that would be effectively resolved if everyone were able to talk in person.

A regular pattern I see from those who are effective in remote-first work is that they ensure regular face-to-face meetings. During these they schedule those elements of work that are done better together. Remote work is more effective for tasks that require solo concentration, and modern tools can make remote pairing workable. But tasks that require lots of input from many people with rapid feedback are much easier to do when everyone is in the same room. No video-conference system can create the that depth of interaction, staring at a computer screen to see what other people are doing is draining, with no opportunity to pop out for a coffee together to break up the work. Debates about product strategy, explorations of systems architecture, explorations of new ground - these are common tasks for when the team is assembled.

For people to work effectively together they need to trust each other, aware of how much they can rely on each other. Trust is hard to develop online, where there isn't the social cues that can happen when we are in the same room. Thus the most valuable part of a face-to-face gathering isn't the scheduled work, it's chitchat while getting a coffee, and conviviality over lunch. Informal conversations, mostly not about work, forge the human contact that makes the work interactions be more effective.

Those guidelines suggest what the content for a face-to-face should be. Working together is both valuable in its own right, and an important part of team bonding. So we should set a full day of work, focusing on those tasks that benefit from the low-latency communication that comes from being together. We should then include what feels like too much time for breaks, informal chatter, and opportunities to step outside the office. I would avoid any artificial “team building” exercises, if only because of how much I hate them. Those who do gatherings like this stress the value from everyone energized afterwards, and thus able to be more effective in the following weeks.

Remote teams can be formed at large distances, and it's common to see members separated by hours of travel. For such teams, the rule of thumb I would use is to get together for a week every two or three months. After the team has become seasoned they may then decide to reduce the frequency, but I would worry if a team isn't having at least two face-to-face meetings a year. If a team is all in the same city, but using a remote-first style to reduce commuting, then they can organize shorter gatherings, and do them more frequently.

This kind of gathering may lead to rethinking of how to configure office space. Much has been made of how offices are far less used since the pandemic. Offices could well become less of a day-to-day workspace, and more a location for these kinds of irregular team gatherings. This leads to a need for flexible and comfortable team gathering spaces.

Some organizations may balk at the costs of travel and accommodation for a team assembly like this, but they should think of it as an investment in the team's effectiveness. Neglecting these face-to-faces leads to teams getting stuck, heading off in the wrong direction, plagued with conflict, and people losing motivation. Compared to this, saving on airplanes and hotels is a false economy.

When working with a legacy system it is valuable to identify and create seams: places where we can alter the behavior of the system without editing source code. Once we've found a seam, we can use it to break dependencies to simplify testing, insert probes to gain observability, and redirect program flow to new modules as part of legacy displacement.

Michael Feathers coined the term “seam” in the context of legacy systems in his book Working Effectively with Legacy Code. His definition: “a seam is a place where you can alter behavior in your program without editing in that place”.

Here's an example of where a seam would be handy. Imagine some code to calculate the price of an order.

// TypeScript
export async function calculatePrice(order:Order) {
  const itemPrices = order.items.map(i => calculateItemPrice(i))
  const basePrice = itemPrices.reduce((acc, i) => acc + i.price, 0)
  const discount = calculateDiscount(order)
  const shipping = await calculateShipping(order)
  const adjustedShipping = applyShippingDiscounts(order, shipping)
  return basePrice + discount + adjustedShipping
}

The function calculateShipping hits an external service, which is slow (and expensive), so we don't want to hit it when testing. Instead we want to introduce a stub, so we can provide a canned and deterministic response for each of the testing scenarios. Different tests may need different responses from the function, but we can't edit the code of calculatePrice inside the test. Thus we need to introduce a seam around the call to calculateShipping, something that will allow our test to redirect the call to the stub.

One way to do this is to pass the function for calculateShipping as a parameter

export async function calculatePrice(order:Order, shippingFn: (o:Order) => Promise<number>) {
  const itemPrices = order.items.map(i => calculateItemPrice(i))
  const basePrice = itemPrices.reduce((acc, i) => acc + i.price, 0)
  const discount = calculateDiscount(order)
  const shipping = await shippingFn(order)
  const adjustedShipping = applyShippingDiscounts(order, shipping)
  return basePrice + discount + adjustedShipping
}

A unit test for this function can then substitute a simple stub.

const shippingFn = async (o:Order) => 113
expect(await calculatePrice(sampleOrder, shippingFn)).toStrictEqual(153)

Each seam comes with an enabling point: “a place where you can make the decision to use one behavior or another” [WELC]. Passing the function as parameter opens up an enabling point in the caller of calculateShipping.

This now makes testing a lot easier, we can put in different values of shipping costs, and check that applyShippingDiscounts responds correctly. Although we had to change the original source code to introduce the seam, any further changes to that function don't require us to alter that code, the changes all occur in the enabling point, which lies in the test code.

Passing a function as a parameter isn't the only way we can introduce a seam. After all, changing the signature of calculateShipping may be fraught, and we may not want to thread the shipping function parameter through the legacy call stack in the production code. In this case a lookup may be a better approach, such as using a service locator.

export async function calculatePrice(order:Order) {
  const itemPrices = order.items.map(i => calculateItemPrice(i))
  const basePrice = itemPrices.reduce((acc, i) => acc + i.price, 0)
  const discount = calculateDiscount(order)
  const shipping = await ShippingServices.calculateShipping(order)
  const adjustedShipping = applyShippingDiscounts(order, shipping)
  return basePrice + discount + adjustedShipping
}

class ShippingServices {
  static #soleInstance: ShippingServices
  static init(arg?:ShippingServices) {
    this.#soleInstance = arg || new ShippingServices()
  }
  static async calculateShipping(o:Order) {return this.#soleInstance.calculateShipping(o)}
  async calculateShipping(o:Order)  {return legacy_calcuateShipping(o)}
  // ... more services

The locator allows us to override the behavior by defining a subclass.

class ShippingServicesStub extends ShippingServices {
  calculateShippingFn: typeof ShippingServices.calculateShipping =
     (o) => {throw new Error("no stub provided")}
  async calculateShipping(o:Order) {return this.calculateShippingFn(o)}
  // more services

We can then use an enabling point in our test

const stub = new ShippingServicesStub()
stub.calculateShippingFn = async (o:Order) => 113
ShippingServices.init(stub)
expect(await calculatePrice(sampleOrder)).toStrictEqual(153)

This kind of service locator is a classical object-oriented way to set up a seam via function lookup, which I'm showing here to indicate the kind of approach I might use in other languages, but I wouldn't use this approach in TypeScript or JavaScript. Instead I'd put something like this into a module.

export let calculateShipping = legacy_calculateShipping

export function reset_calculateShipping(fn?: typeof legacy_calculateShipping) {
  calculateShipping = fn || legacy_calculateShipping
}

We can then use the code in a test like this

const shippingFn = async (o:Order) => 113
reset_calculateShipping(shippingFn)
expect(await calculatePrice(sampleOrder)).toStrictEqual(153)

As the final example suggests, the best mechanism to use for a seam depends very much on the language, available frameworks, and indeed the style of the legacy system. Getting a legacy system under control means learning how to introduce various seams into the code to provide the right kind of enabling points while minimizing the disturbance to the legacy software. While a function call is a simple example of introducing such seams, they can be much more intricate in practice. A team can spend several months figuring out how to introduce seams into a well-worn legacy system. The best mechanism for adding seams to a legacy system may be different to what we'd do for similar flexibility in a green field.

Feathers's book focuses primarily on getting a legacy system under test, as that is often the key to being able to work with it in a sane way. But seams have more uses than that. Once we have a seam, we are in the position to place probes into the legacy system, allowing us to increase the observability of the system. We might want to monitor calls to calculateShipping, figuring out how often we use it, and capturing its results for separate analysis.

But probably the most valuable use of seams is that they allow us to migrate behavior away from the legacy. A seam might redirect high-value customers to a different shipping calculator. Effective legacy displacement is founded on introducing seams into the legacy system, and using them to gradually move behavior into a more modern environment.

Seams are also something to think about as we write new software, after all every new system will become legacy sooner or later. Much of my design advice is about building software with appropriately placed seams, so we can easily test, observe, and enhance it. If we write our software with testing in mind, we tend to get a good set of seams, which is a reason why Test Driven Development is such a useful technique.

Throughout my career, people have compared software development to “traditional” engineering, usually in a way to scold software developers for not doing a proper job. As someone who got his degree in Electronic Engineering, this resonated with me early in my career. But this way of thinking is flawed because most people have the wrong impression of how engineering works in practice.

Glenn Vanderburg has spent a lot of time digging into these misconceptions, and I strongly urge anyone who wants to compare software development to engineering to watch his talk Real Software Engineering. It's also well worth listening to his interview on the podcast Oddly Influenced. Sadly I've not been able to persuade him to write this material down - it would make a great article.

Another good thinker on this relationship is Hillel Wayne. He interviewed a bunch of “crossovers” - people who had worked both in traditional engineering and in software. He wrote up what he learned in a series of essays, starting with Are We Really Engineers?

Test-Driven Development (TDD) is a technique for building software that guides software development by writing tests. It was developed by Kent Beck in the late 1990's as part of Extreme Programming. In essence we follow three simple steps repeatedly:

• Write a test for the next bit of functionality you want to add.
• Write the functional code until the test passes.
• Refactor both new and old code to make it well structured.

Although these three steps, often summarized as Red - Green - Refactor, are the heart of the process, there's also a vital initial step where we write out a list of test cases first. We then pick one of these tests, apply red-green-refactor to it, and once we're done pick the next. Sequencing the tests properly is a skill, we want to pick tests that drive us quickly to the salient points in the design. During the process we should add more tests to our lists as they occur to us.

Writing the test first, what XPE2 calls Test-First Programming, provides two main benefits. Most obviously it's a way to get SelfTestingCode, since we can only write some functional code in response to making a test pass. The second benefit is that thinking about the test first forces us to think about the interface to the code first. This focus on interface and how you use a class helps us separate interface from implementation, a key element of good design that many programmers struggle with.

The most common way that I hear to screw up TDD is neglecting the third step. Refactoring the code to keep it clean is a key part of the process, otherwise we just end up with a messy aggregation of code fragments. (At least these will have tests, so it's a less painful result than most failures of design.)

Regression bugs are newly appeared bugs in features of the software that have been around for a while. When hunting them, it usually valuable to figure out which change in the software caused them to appear. Looking at that change can give invaluable clues about where the bug is and how to squash it. There isn't a well-known term for this form of investigation, but I call it Diff Debugging.

Diff debugging only works if we have our code in version control, but fortunately these days that's the norm. But there are some more things that are needed to make it work effectively. We need Reproducible Builds, so that we can run old versions of the software easily. It helps greatly to have small commits, due to high-frequency integration. That way when we find the guilty commit, we can more easily narrow down what happened.

To find the commit that bred the bug, we begin by finding any past version without the bug. Mark this as a last-good version and the current version as the earliest-bad. Then find the commit half-way between the two and see if the bug is there. If so then this commit becomes the earliest-bad, otherwise it becomes the last-good. Repeat the process (which is a “half-interval” or “binary” search) until we've got the guilty commit.

If we use git, then the git bisect command will automate much of this for us. If we can write a test that will show the presence of the bug, then git bisect can use that too, automating the whole process of finding the guilty commit.

I often find diff debugging to be useful within a programming session. If I have slow tests that take a few minutes to run, I might program for half-an-hour running only a subset of the most relevant tests. As long as I commit after every green test run, I can use diff debugging should one of those slower tests fail. Such is the value of committing extremely frequently, even if they are so small that I feel its best to squash them for the long-term history. Some IDEs make this easier by keeping a local history automatically that is finer-grained than the commits to version control.

Any large software effort, such as the software estate for a large company, requires a lot of people - and whenever you have a lot of people you have to figure out how to divide them into effective teams. Forming Business Capability Centric teams helps software efforts to be responsive to customers’ needs, but the range of skills required often overwhelms such teams. Team Topologies is a model for describing the organization of software development teams, developed by Matthew Skelton and Manuel Pais. It defines four forms of teams and three modes of team interactions. The model encourages healthy interactions that allow business-capability centric teams to flourish in their task of providing a steady flow of valuable software.

The primary kind of team in this framework is the stream-aligned team, a Business Capability Centric team that is responsible for software for a single business capability. These are long-running teams, thinking of their efforts as providing a software product to enhance the business capability.

Each stream-aligned team is full-stack and full-lifecycle: responsible for front-end, back-end, database, business analysis, feature prioritization, UX, testing, deployment, monitoring - the whole enchilada of software development. They are Outcome Oriented, focused on business outcomes rather than Activity Oriented teams focused on a function such as business analysis, testing, or databases. But they also shouldn't be too large, ideally each one is a Two Pizza Team. A large organization will have many such teams, and while they have different business capabilities to support, they have common needs such as data storage, network communications, and observability.

A small team like this calls for ways to reduce their cognitive load, so they can concentrate on supporting the business needs, not on (for example) data storage issues. An important part of doing this is to build on a platform that takes care of these non-focal concerns. For many teams a platform can be a widely available third party platform, such as Ruby on Rails for a database-backed web application. But for many products there is no single off-the-shelf platform to use, a team is going to have to find and integrate several platforms. In a larger organization they will have to access a range of internal services and follow corporate standards.

This problem can be addressed by building an internal platform for the organization. Such a platform can do that integration of third-party services, near-complete platforms, and internal services. Team Topologies classifies the team that builds this (unimaginatively-but-wisely) as a platform team.

Smaller organizations can work with a single platform team, which produces a thin layer over an externally provided set of products. Larger platforms, however, require more people than can be fed with two-pizzas. The authors are thus moving to describe a platform grouping of many platform teams.

An important characteristic of a platform is that it's designed to be used in a mostly self-service fashion. The stream-aligned teams are still responsible for the operation of their product, and direct their use of the platform without expecting an elaborate collaboration with the platform team. In the Team Topologies framework, this interaction mode is referred to as X-as-a-Service mode, with the platform acting as a service to the stream-aligned teams.

Platform teams, however, need to build their services as products themselves, with a deep understanding of their customer's needs. This often requires that they use a different interaction mode, one of collaboration mode, while they build that service. Collaboration mode is a more intensive partnership form of interaction, and should be seen as a temporary approach until the platform is mature enough to move to x-as-a service mode.

So far, the model doesn't represent anything particularly inventive. Breaking organizations down between business-aligned and technology support teams is an approach as old as enterprise software. In recent years, plenty of writers have expressed the importance of making these business capability teams be responsible for the full-stack and the full-lifecycle. For me, the bright insight of Team Topologies is focusing on the problem that having business-aligned teams that are full-stack and full-lifecycle means that they are often faced with an excessive cognitive load, which works against the desire for small, responsive teams. The key benefit of a platform is that it reduces this cognitive load.

This insight has profound implications. For a start it alters how platform teams should think about the platform. Reducing client teams' cognitive load leads to different design decisions and product roadmap to platforms intended primarily for standardization or cost-reduction. Beyond the platform this insight leads Team Topologies to develop their model further by identifying two more kinds of team.

Some capabilities require specialists who can put considerable time and energy into mastering a topic important to many stream-aligned teams. A security specialist may spend more time studying security issues and interacting with the broader security community than would be possible as a member of a stream-aligned team. Such people congregate in enabling teams, whose role is to grow relevant skills inside other teams so that those teams can remain independent and better own and evolve their services. To achieve this enabling teams primarily use the third and final interaction mode in Team Topologies. Facilitating mode involves a coaching role, where the enabling team isn't there to write and ensure conformance to standards, but instead to educate and coach their colleagues so that the stream-aligned teams become more autonomous.

Stream-aligned teams are responsible for the whole stream of value for their customers, but occasionally we find aspects of a stream-aligned team's work that is sufficiently demanding that it needs a dedicated group to focus on it, leading to the fourth and final type of team: complicated-subsystem team. The goal of a complicated-subsystem team is to reduce the cognitive load of the stream-aligned teams that use that complicated subsystem. That's a worthwhile division even if there's only one client team for that subsystem. Mostly complicated-subsystem teams strive to interact with their clients using x-as-a service mode, but will need to use collaboration mode for short periods.

Team Topologies includes a set of graphical symbols to illustrate teams and their relationships. These shown here are from the current standards, which differ from those used in the book. A recent article elaborates on how to use these diagrams.

Team Topologies is designed explicitly recognizing the influence of Conways Law. The team organization that it encourages takes into account the interplay between human and software organization. Advocates of Team Topologies intend its team structure to shape the future development of the software architecture into responsive and decoupled components aligned to business needs.

George Box neatly quipped: “all models are wrong, some are useful”. Thus Team Topologies is wrong: complex organizations cannot be simply broken down into just four kinds of teams and three kinds of interactions. But constraints like this are what makes a model useful. Team Topologies is a tool that impels people to evolve their organization into a more effective way of operating, one that allows stream-aligned teams to maximize their flow by lightening their cognitive load.