Is Static Analysis Able to Identify Unnecessary Source Code?
Roman Haas, Rainer Niedermayr, Tobias Roehm, Sven Apel
Abstract
Roman Haas, Rainer Niedermayr, Tobias Roehm, Sven Apel
Abstract
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.
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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.
Key concepts: Computer science, Code review, Static program analysis, Source code, KPI-driven code analysis, Centrality, Code (set theory), Oracle