We identify two sorts of software development metrics in our software development practice: those that aid in evaluating the final product and those that provide insight into the development process. In this article, Hanna Shnaider, the Head of Marketing at FortySeven, discusses defining and applying successful metrics of both types.
How to Apply Software Development Metrics?
Project owners can use software development metrics, managers, developers, and quality assurance teams to:
Management and planning of projects
Management relies heavily on metrics. Software development metrics provide a clear picture of what and how teams have performed in past project iterations. A project manager or custom software agency may better estimate and plan a budget, time, resources, and requirements for subsequent iterations based on the data and quickly determine if an iteration or the entire project goes wrong based on the data.
Metrics allow the project owner or custom software development companies to easily comprehend and assess the project’s current state, challenges, and solutions.
Prioritization of tasks
Metrics are a useful technique to determine which tasks should be completed in which sequence to maximize value. If, for example, customer happiness is low due to ongoing dissatisfaction with the quality of software upgrades that cause problems, it may be time to start devoting more time during each iteration to regression testing rather than delivering a large number of new features.
Metrics can assist in determining whether changing a strategy, a practice, a tool, or anything else adds value, how much benefit can be expected, and how it connects to the investments made. When transitioning to DevOps, for example, KPIs like the amount of failed changes/deployments and mean time to recovery (MTTR) might aid in evaluating the benefits of the shift.
Monitoring and reporting on SLAs, as well as service adjustments
With KPIs, a custom software development firm fortyseven47.com could effectively communicate and monitor the worth they assume from an outsourcing merchant (a rise in the intensity of releases, optimized test coverage, a reduction in the number of functionalities waiting in the backlog longer than the deadline, or a reduction in the number of defects discovered in user acceptance testing (UAT) / in production) and understand how productive outsourced custom software developer teams are. A seller, on the other hand, may vividly demonstrate the enhancements that have been made.
What to Measure?
Before we begin, it’s important to note that the metrics list should be determined on a case-by-case basis. It is an outrageous waste of time and effort to merely track whatever a project management tool gives or a custom software development framework recommends or duplicate the metrics provided from another project blindly. Avoid metrics that don’t answer any specific questions from project stakeholders or whose outcomes don’t have any potential impact on the project process. For example, performance measurements will be a major emphasis for a real-time processing system, whereas metrics for a distributed asynchronous system will focus on availability.
The solution’s quality when delivered
The exterior qualities of software (reliability, maintainability, and so on) are of relevance to software development company stakeholders. Modern software quality can be represented by eight basic criteria, according to ISO/IEC 25010: being sent to requirements, reliability, maintainability, compatibility, portability, security, usability, and performance. Each feature can be further broken into a collection of characteristics, requiring the tracking of a large number of metrics to provide a complete picture.
The custom software development teams, project managers, and project owners are all interested in knowing the code quality. As a result, team leads, architects, and developers are interested in KPIs that might shed light on the project’s technical aspects, such as algorithmic complexity, the number of extra dependencies, code churn, code duplication, test coverage, defect density, and so on.
The project manager’s primary focus will be on tracking costs, resources, timeframes, and performance. They must also comprehend the efficacy of the current development techniques. All programming paradigm, software development model, and framework would have its own set of success indicators: for linear (traditional) development by utilizing a fixed scope, it’ll be the percentage of scope completed, whereas agile and lean processes will require measurements of available lead time, cycle time, team velocity, and so on.
It’s also critical to assess the satisfaction of the intended consumers. Development companies or customer satisfaction score will be used for public products, while employee feedback will be used for internal applications. In both circumstances, criteria such as interface consistency, interaction appeal, message clarity, interface element clarity, and function interpretation are appropriate.
How Do I Decide on the Metrics to Track?
Keeping track of all conceivable indicators necessitates a large investment of time and money. To create an effective list of metrics, a PM or QA engineer should consider the main success elements for software and the project process (in collaboration with a project owner and mobile app development business stakeholders as appropriate). They should next do a root cause analysis of each attribute before moving on to the actual data needed to track the selected aspect:
“An eCommerce system that is down for 15 minutes can cost us up to $20,000 in lost sales. To maintain the predicted amount of revenue, we should endeavor to improve system availability. What factors influence availability? The amount of failures, the period of failures, the time it takes to restore service, and so on” — this is the proper way to come up with a list of required metrics.
As you can see, various circumstances influence quality attributes, each of which necessitates the tracking of a specific set of metrics to obtain robust data. Let’s use the concept of dependability as an example. Software maturity is one of its properties. Lack of Cohesion in Methods, Improvement of Lack of Cohension Method, and Tight Class Cohesion are closely related measures. It’s also important to keep track of things like the Depth of Inheritance (DIT), the Lack of Documentation (LOD), the Response for a Class (RFC), and so on.
How Do You Implement Metrics?
Following the creation of a list of critical high-level software aspects to monitor, the proper metrics implementation procedure will proceed as follows:
Choosing a formula for metrics
You can use commercially available formulas, such as Halstead’s Metrics, McCabe’s Cyclomatic Complexity, and Albrecht’s Function Point Analysis. You can also develop custom formulas, which are particularly useful for complex attributes.
Some metrics, such as the number of features produced during a sprint, will not require complex calculations and are, at their core, a simple adaption of measurement.
Identifying the measurement’s input data (and making sure it’s available for tracking.
Choosing where to collect data for measures
Specific data owners may be able to offer the information. Testers like FortySeven IT team, for example, can contribute information about test coverage and planned/executed test cases, while end-users can provide information about issues they’ve experienced when interacting with custom software. Project management, source control management systems, CI and CD, application performance management, and company or business intelligence tools are all examples of data sources. End users could provide data immediately via social media interactions (for products), survey forms, checklists, and evaluation questionnaires.
Appointing software development companies like the FortySeven software professionals to be in charge of metric tracking.
Choosing who the results should be shared with (for whom the received data will bring value).
Choosing how frequently to report.
Developing a mitigation strategy if metrics reflect disappointing performance.
Adjusting metrics that aren’t working regularly.
It’s very hard to come up with an optimal set of metrics on the first try; instead, it grows over the project’s life through trial and error.