Challenges for static analysis of Java reflection - literature review and empirical study
The behavior of software that uses the Java Reflection API is fundamentally hard to predict by analyzing code. Only recent static analysis approaches can resolve reflection under unsound yet pragmatic assumptions. We survey what approaches exist and what their limitations are. We then analyze how real-world Java code uses the Reflection API, and how many Java projects contain code challenging state-of-The-Art static analysis. Using a systematic literature review we collected and categorized all known methods of statically approximating reflective Java code. Next to this we constructed a representative corpus of Java systems and collected descriptive statistics of the usage of the Reflection API. We then applied an analysis on the abstract syntax trees of all source code to count code idioms which go beyond the limitation boundaries of static analysis approaches. The resulting data answers the research questions. The corpus, the tool and the results are openly available. We conclude that the need for unsound assumptions to resolve reflection is widely supported. In our corpus, reflection can not be ignored for 78% of the projects. Common challenges for analysis tools such as non-exceptional exceptions, programmatic filtering meta objects, semantics of collections, and dynamic proxies, widely occur in the corpus. For Java software engineers prioritizing on robustness, we list tactics to obtain more easy to analyze reflection code, and for static analysis tool builders we provide a list of opportunities to have significant impact on real Java code.
|Keywords||Empirical Study, Java, Reflection, Static Analysis, Systematic Literature Review|
|Conference||International Conference on Software Engineering|
Landman, D, Serebrenik, A, & Vinju, J.J. (2017). Challenges for static analysis of Java reflection - literature review and empirical study. In Proceedings - 2017 IEEE/ACM 39th International Conference on Software Engineering, ICSE 2017 (pp. 507–518). doi:10.1109/ICSE.2017.53