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Replacing an ALM system shouldn’t mean losing years of engineering history. Learn why legacy data retention matters, why common workarounds fail, and how purpose-built ALM data archiving helps organizations preserve traceability, compliance, and institutional knowledge after migration.
The software industry is experiencing unprecedented growth, driven by digital transformation. Software quality has thus become a strategic imperative.
The 15th World Quality Report underscores this shift, highlighting the growing emphasis on quality engineering and its integration into core business operations.
With a focus on delivering value rather than volume, 67% of companies are prioritizing quality assurance (QA) as a cornerstone of their operations.
To thrive in today’s quality-first software landscape, a lot comes down to setting the right benchmarks for measuring success.
In this blog, we take a deep-dive to bring you up to speed with the software quality metrics that you should essentially measure – and the critical need for Quality Gap Intelligence to maximize your end-to-end SDLC potential.
The three core dimensions of software quality are:
To achieve optimal software quality, organizations must adopt a holistic approach that incorporates the following strategies:
Pro Tip: To achieve a truly holistic view of software quality, it’s essential to embed quality metrics directly within existing development and work management tools. By visualizing quality data alongside development progress, teams can proactively address quality issues from the get-go.
Pro Tip: Measure test coverage for new or modified code. This targets testing efforts on areas most likely to introduce defects, reducing overall test execution time as well.
Pro Tip: By incorporating work item IDs into commit messages, software teams can trace the number of times a file and a line was touched for ‘Bug’ type workitems.
This practice establishes a direct link between code changes and the corresponding defects or user stories, allowing for efficient root cause analysis.
Pro Tip: Get defects per change by team, module, developer, reviewer, etc. The best way to ensure traceability is by linking work items to defects on a project, portfolio level.
Pro Tip: With Requirement-Test traceability – Measure test effort and test reliability by product modules, key functionalities, by product teams etc.
Pro Tip: Analyse test case history to understand how often test cases are revised, how often new test cases are added, etc.
Interestingly, a study by IBM shows that the cost of fixing a defect multiplies as it progresses through the development lifecycle.
This further emphasizes the critical importance of early defect detection and prevention.
Pro Tip: Analyze historical data to pinpoint areas where defects consistently slip through the testing net. Also, calculate the percentage of defects found in production compared to those discovered in pre-production environments.
Pro Tip: Make DORA metrics actionable by tracing lead time for changes, change failure rate and mean time to recover back to assignee, teams, product areas etc. Adjust risk indicators for tests and source code areas depending on history of changes that contributed to increased failure rate.
Change impact analysis refers to how code alterations affect the system. It helps identify risks, manage dependencies, and ensure smooth deployments minimizing disruptions. Developers and testers can give an objective spin to test planning if they know how a change impacts existing functionalities.
Change Impact focuses on ensuring that changes to the codebase do not negatively impact existing functionalities. To prioritize risky areas by measuring the coverage of impacted functionalities – teams can prioritize potential regression areas and reduce testing efforts accordingly.
Scope churn refers to the instability or frequent changes in project requirements or features during a release cycle. High scope churn can negatively impact project timelines, budgets, and quality.
Code override occurs when multiple developers modify the same code section. This can increase the complexity of code changes and the potential for introducing defects.
Measures source code areas that are changed frequently, has long dev and test cycles, is touched by multiple developers, has history of high product defects and so on. Using this metric ensures you are investing in the most painful technical debt and prioritizing resources effectively.
To effectively harness the power of these metrics and transform software quality, organizations need a unified, intelligent platform. This is where Quality Gap Intelligence (QGI) solutions like OpsHub Insights come into play.
OpsHub Insights empowers teams to:
Minimize Defects, Accelerate Releases
Muskaan works as a Content and SEO Strategist at OpsHub. Her interests include devising content marketing strategies for SaaS enterprises, brand strategy and the convergence of product-first thinking with emerging tech and communication.
