Faster execution of tasks. On average, data analysts spend 80% of their time cleaning and sorting out data instead of focusing on the analysis. At the same time, software quality indicators speed up data processing and allow specialists to spend their efforts on solving more urgent tasks.
Clarity in all processes. Having visibility of what’s going on at each stage of the product’s building, testing, and maintenance are the keys to identifying and resolving misunderstandings in complex projects. Providing specific QA standards up front will help keep the team on the same page.
Facilitating prioritization. By using a set of relevant metrics, you make life easier for the PM, whose primary responsibility is to track the project’s progress and lead the team in the right direction. Based on the results of the analysis, project managers can rationally prioritize tasks instead of following intuition.
Effective management. To ensure that your software product is on schedule, you need metrics. Anticipate possible risks, estimate the timing of future steps, and make changes along the way when necessary.
Better decision-making. Metrics bring objectivity to the table, so you avoid impulsive decisions and costly mistakes. Instead, your project will be optimized to meet your software quality assurance goals.
To assess the cyclomatic complexity of your codebase, various tools are available such as CodeClimate, SonarQube, and Understand. These tools not only calculate the complexity but also offer suggestions to simplify the code, thereby enhancing its maintainability.
Code coverage measurement can be done using tools like JaCoCo, Cobertura, and Istanbul. These tools provide detailed reports indicating which parts of the code are covered by tests and which are not.
Code churn analysis can be done with Git, a widely used version control system. By using Git's built-in features like git log and git diff, you can track changes made to your codebase and detect sections undergoing frequent changes.
Code duplication detection can be accomplished using PMD, CPD, and Simian tools that can detect sections of code that are similar or identical to others in the codebase.
Maintainability index can be calculated by tools like CodeClimate, SonarQube, and Understand by analyzing various factors including cyclomatic complexity, code duplication, and code coverage. This index provides an overall measure of the maintainability of the codebase.