Grown software systems often contain code that is not necessary anymore. Such unnecessary code wastes resources during development and maintenance, for example, when preparing code for migration or certification. Running a profiler may reveal code that is not used in production, but it is often time-consuming to obtain representative data in this way. We investigate to what extent a static analysis approach, which is based on code stability and code centrality, is able to identify unnecessary code and whether its recommendations are relevant in practice. To study the feasibility and usefulness of our approach, we conducted a study involving 14 open-source and closed-source software systems. As there is no perfect oracle for unnecessary code, we compared recommendations for unnecessary code with historical cleanups, runtime usage data, and feedback from 25 developers of five software projects. Our study shows that recommendations generated from stability and centrality information point to unnecessary code that cannot be identified by dead code detectors. Developers confirmed that 34% of recommendations were indeed unnecessary and deleted 20% of the recommendations shortly after our interviews. Overall, our results suggest that static analysis can provide quick feedback on unnecessary code and is useful in practice.

Mutation testing is a means to assess the effectiveness of a test suite and its outcome is considered more meaningful than code coverage metrics. However, despite several optimizations, mutation testing requires a significant computational effort and has not been widely adopted in industry. Therefore, we study in this paper whether test effectiveness can be approximated using a more light-weight approach. We hypothesize that a test case is more likely to detect faults in methods that are close to the test case on the call stack than in methods that the test case accesses indirectly through many other methods. Based on this hypothesis, we propose the minimal stack distance between test case and method as a new test measure, which expresses how close any test case comes to a given method, and study its correlation with test effectiveness. We conducted an empirical study with 21 open-source projects, which comprise in total 1.8 million LOC, and show that a correlation exists between stack distance and test effectiveness. The correlation reaches a strength up to 0.58. We further show that a classifier using the minimal stack distance along with additional easily computable measures can predict the mutation testing result of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can be taken into consideration as a light-weight alternative to mutation testing or as a preceding, less costly step to that.

Teamscale is a software intelligence platform, that is, it creates transparency on code quality and the underlying software development process. This makes it possible for developers, testers and managers to better understand and control technical debt of their systems. In this paper, we give an overview of Teamscale and how this tool can be used in practice to control and lower technical debt in the long run. We explain which code analyses can be used to identify and address technical debt. Teamscale is available for free for research and teaching purposes at www.teamscale.io.