The volume of software worldwide continues to grow exponentially, and software quality has simply not kept pace. This has caused the industry to reach a crisis point in terms of mounting technical debt that has been caused by the continual development of software without the correct quality control processes in place.
The four biggest contributing factors of technical debt are bad architecture choices; overly complex code; lack of code documentation; and inadequate testing. As technical debt increases, developers spend a majority of their time fixing bugs and struggling with fragile code, rather than building new features. Compounding the technical debt and related quality issues are the following:
- Many new applications rely on legacy code for significant functionality. Without adequate tests, modification of legacy code will cause regression.
- Software is built by distributed teams, which leads to compartmentalized tests. If tests are not shared and all team members don’t have access to up-to-date information on test status, bugs are often found too late in the release cycle.
- Application sizes are increasing, and software code bases have gotten larger and more complex. Because of this, a huge number of tests are needed to ensure quality.
To continue developing the volume of software that modern products require, companies must develop scalable development processes that lead to robust and maintainable code bases relatively free of technical debt. How can we accomplish this? For groups that want to improve software quality, often the hardest question to answer is: ‘where do we start?’
Just like regular health exams can help find problems earlier, when the chances for treatment and cure are better, the same is true for software code. A software health check reviews the current state of an organization’s testing infrastructure and gathers key metrics such as gaps in testing, code complexity, and comment density to identify risk areas. Once complete, a detailed report is provided containing all of the data along with recommendations for improvement. This helps to provide an independent professional assessment of the testing infrastructure without shifting focus or time away from development goals.
Business success is also driven by actionable intelligence that is derived from accurate and up-to-date data. Having a wealth of data that can be used to make quality improvement and release decisions enables development teams to automate all test activities, shorten test times, and generate real-time metrics on release readiness.
QA tools that integrate with an organization’s existing build system and test infrastructure can be used to collect the key metrics needed such as code complexity, frequency of code changes, test case status, and code coverage information. Using data gathered from the build system and from monitoring system test activities, these tools can identify correlations between tests and code. As the code changes, the minimum set of tests required to provide complete testing of the changes can be automatically computed.
Furthermore, using a web-based dashboard of software code quality and testing completeness metrics enables users to identify trends in a single codebase or compare metrics between multiple codebases.
Software quality and testing completeness are linked. You cannot have quality without complete testing.
Vector Software provides the world's leading brands with an embedded testing platform that allows them to develop high quality software, accelerate time-to-market, and reduce the life-cycle maintenance costs of their embedded software applications.