For organizations all over the world, the eternal question is how to measure the software development team’s productivity. There’s been a lot of attempts to answer this, yet a robust measure continues eluding the industry. Counting output, for instance, such as the number of code lines produced is not sufficient since there’s not much point in counting lines that could be defective.
Also, it’s not easy to quantify input. Do you count the number of hours coding, or the number of individuals, and the total hours spent? What exactly is development productivity? Before we dig further, let’s find out what exactly is a metric in developing software.
A software metric means a potential area in which measurement could be applied to a specific software module or its specifications in an effective manner. Simply put, a metric assumes taking several data from the app development lifecycle and utilizing it to measure productivity. It could originate from one or numerous data sources.
Any data point traceable could be a metric for measuring the performance of the team of developers of a software development company. The majority of measuring tools are equipped with analytics units and dashboards already, which you could set up to keep tabs on everything and anything.
Major Factors to Measure Software Development Productivity
You can train the development team to do their best work effectively and fast. If the end product however is software that works but has no business value, then you’re pursuing incorrect metrics. In measuring productivity, you have to track both agile and business metrics.
1. Sprint Burndown
One of the major agile scrum metrics. A burndown report communicates complexity in work through the sprint that’s based on story points. The team’s goal is to deliver all work consistently, based on the forecast. By keeping track of this metric, you could obtain critical insights.
Consistent early sprint finishes could indicate a lack of scheduled work for a sprint. Missing sprint deadlines consistently, on the other hand, could indicate a gap in your planning, as well as the fact that your team is requested to deliver a lot of work. The feature of the report should be a steep reduction in ‘remaining values’, instead of a dramatic drop since the latter indicates that the work wasn’t assigned in granular pieces.
2. Throughput factor
An indication of the team’s whole value-added work output. Typically, it’s represented by the work unites that the team has accomplished within a specific time. You have to align the throughput metric with your present business goals.
If your goal is to release new modules that are bug-free in this sprint, you should see a huge part of defect tickets being resolved, and so on. Moreover, throughput measurement helps in:
- understanding when a team is overloaded when comparing the average throughput versus the current workload
- detecting when a team is blocked when the throughout metric drops.
To take it a step further, you could monitor additional performance metrics of development for app development management, including:
- customer and operation effect of defects
- delivery date
- integrity of code
- communication quality
- system ability on meeting service levels
- canceled defects, which get rid of wasted Q&A time
3. Team Velocity Metric
It accounts for the ‘amount’ of software that the team completes on a sprint. It could be measured in hours or points, and this metric could be used for planning and estimation. By velocity measurement, you could:
- understanding if the team is blocked
- set better expectations of delivery and a realistic forecast of a sprint
- investigating if the change in the process have any results
- see unforeseen challenges that weren’t accounted for when planning a sprint
You also have to pay attention to the velocity’s volatility. If you consistently score high volatility, it means that a few processes are working now, and you have to investigate that. Every team has a specific estimation culture and could have a difference of story points that you have to account for.
4. Cycle Time
It stands for the entire time that elapses from the time when the work started until it’s being completed. This metric enables you to estimate how fast you could deliver to your users the new features. Furthermore, it’s another way of understanding the current speed of the team for various tasks by breaking down the entire throughput to median time by issue type or status.
Why Measuring Productivity in Software Development is Important
For developing software solutions, productivity metrics clarify performance expectations. Your development team stays engaged since they exactly know what’s expected of them at work. Metrics get to communicate expectations and will teach you how to measure unbiased developer productivity.
Moreover, it helps you determine the factors that prevent the effectiveness of the team and eliminate those that ultimately could lead to a high-performing, happier team. When you measure productivity, you also identify the areas of a business that are weak or lag behind. Productivity helps locate the weak links and provides a great opportunity for a brand to make the best of the information and fix the weak business aspects more effectively, with strategies that are directly implemented.
Productivity measurement helps an organization provide timely feedback when it comes to areas where it’s needed. Timey feedback helps the workforce adjust and align its efforts toward the right direction, and move forward into consistently attaining business goals. In all, productivity in developing a software solution takes the guesswork out and facilitates communication within a company.
These factors help a business in a lot of wonderful ways to boost brand performance, productivity gains, and ultimately increase business results.
Each and every project would have unique difficulties and complexity. Although you need to establish several software productivity metrics that you would consistently track, leave them open for discussion with the team and the key stakeholders in your organization. It becomes relevant to understand software productivity in systems analysis when you take into account that good systems analysis boosts productivity, and software productivity is a measure of success.