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Productivity

When we think of productivity, we usually have a vague concept of a rate of work per unit time. Where it gets tricky is in defining "work". On an individual level, work can be easier to define, because developers often have specific concrete tasks that they're assigned. But until they're not, it's not really easy to define progress (well, it's not that easy to define "done" sometimes either, but that's a topic for a later chapter). When you start considering work at the scale of a team or an organization, productivity gets even harder to define, since an individual's productivity might be increased by ignoring every critical request from a teammate, harming the team's overall productivity.

Despite the challenge in defining productivity, there are numerous factors that affect productivity. For example, at the individual level, having the right tools can result in an order of magnitude difference in speed at accomplishing a task. One study I ran found that developers using the Eclipse IDE spent a third of their time just physically navigating between source files (Ko et al. 2005). With the right navigation aids, developers could be writing code and fixing bugs 30% faster. In fact, some tools like Mylyn automatically bring relevant code to the developer rather than making them navigate to it, greatly increasing the speed which with developers can accomplish a task (Kersten & Murphy 2006). Long gone are the days when developers should be using bare command lines and text editors to write code: IDEs can and do greatly increase productivity when used and configured with speed in mind.

That said, productivity is not just about individual developers. Because communication is a key part of team productivity, an individual's productivity is as much determined by their ability to collaborate and communicate with other developers. In a study spanning dozens of interviews with senior software engineers, Li et al. found that the majority of critical attributes for software engineering skill (productivity included) concerned their interpersonal skills, their communication skills, and their ability to be resourceful within their organization (Li et al. 2015). Similarly, LaToza et al. found that the primary bottleneck in productivity was communication with teammates, primarily because waiting for replies was slower than just looking something up (LaToza et al. 2006). Of course, looking something up has its own problems. While StackOverflow is an incredible resource for missing documentation (Mamykina et al. 2001), it also is full of all kinds of misleading and incorrect information contributed by developers without sufficient expertise to answer questions (Barua et la. 2014). Finally, because communication is such a critical part of retrieving information, adding more developers to a team has surprising effects. One study found that adding people to a team slowly enough to allow them to onboard effectively could reduce defects, but adding them too fast led to increases in defects (Meneely et al. 2011).

Another dimension of productivity is learning. Great engineers are resourceful, quick learners (Li et al. 2015); new engineers must be even more resourceful, even though their instincts are often to hide their lack of expertise from exactly the people they need help from (Begel & Simon 2008). Experienced developers know that learning is important and now rely heavily on social media such as Twitter to follow industry changes, build learning relationships, and discover new concepts and platforms to learn (Singer et al. 2012).

Unfortunately, learning is no easy task. One of my earliest studies as a researcher investigated the barriers to learning new programming languages and systems, finding six distinct types of content that are challenging (Ko & Myers 2004). To use a programming platform successfully, people need to overcome design barriers, which are the abstract computational problems that must be solved, independent of the languages and APIs. People need to overcome selection barriers, which involve finding the right abstractions or APIs to achieve the design they have identified. People need to overcome use and coordination barriers, which involve operating and coordinating different parts of a language or API together to achieve novel functionality. People need to overcome comprehension barriers, which involve knowing what can go wrong when using part of a language or API. And finally, people need to overcome information barriers, which are posed by the limited ability of tools to inspect a program's behavior at runtime during debugging. Every single one of these barriers has its own challenges, and developers encounter them every time they are learning a new platform, regardless of how much expertise they have.

Aside from individual and team factors, productivity is also influenced by the particular features of a project's code, how the project is managed, or the environment and organizational culture in which developers work (Vosburgh et al. 1984, DeMarco & Lister 1985). In fact, these might actually be the biggest factors in determining developer productivity. This means that even a developer that is highly productive individually cannot rescue a team that is poorly structured working on poorly architected code. This might be why highly productive developers are so difficult to recruit to poorly managed teams.

A different way to think about productivity is to consider it from a "waste" perspective, in which waste is defined as any activity that does not contribute to a product's value to users or customers. Sedano et al. investigated this view across two years and eight software development projects in a software development consultancy (Sedano et al. 2017), contributing a taxonomy of waste:

• Building the wrong feature or product. The cost of building a feature or product that does not address user or business needs.
• Mismanaging the backlog. The cost of duplicating work, expediting lower value user features, or delaying nec- essary bug fixes.
• Rework. The cost of altering delivered work that should have been done correctly but was not.
• Unnecessarily complex solutions. The cost of creating a more complicated solution than necessary, a missed opportu- nity to simplify features, user interface, or code.
• Extraneous cognitive load. The costs of unneeded expenditure of mental energy, such as poorly written code, context switching, confusing APIs, or technical debt.
• Psychological distress. The costs of burdening the team with unhelpful stress arising from low morale, pace, or interpersonal conflict.
• Waiting/multitasking. The cost of idle time, often hidden by multi-tasking, due to slow tests, missing information, or context switching.
• Knowledge loss. The cost of re-acquiring information that the team once knew.
• Ineffective communication. The cost of incomplete, incorrect, mislead- ing, inefficient, or absent communication.

One could imagine using these concepts to refine processes and practices in a team, helping both developers and managers be more aware of sources of waste that harm productivity.

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