Traceclipse: an eclipse plug-in for traceability link recovery and management
Proceedings of the 6th International Workshop on Traceability in Emerging Forms of Software Engineering
Recovering test-to-code traceability using slicing and textual analysis
Journal of Systems and Software
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Unit tests are valuable as a source of up-to-date documentation as developers continuously changes them to reflect changes in the production code to keep an effective regression suite. Maintaining traceability links between unit tests and classes under test can help developers to comprehend parts of a system. In particular, unit tests show how parts of a system are executed and as such how they are supposed to be used. Moreover, the dependencies between unit tests and classes can be exploited to maintain the consistency during refactoring. Generally, such dependences are not explicitly maintained and they have to be recovered during software development. Some guidelines and naming conventions have been defined to describe the testing environment in order to easily identify related tests for a programming task. However, very often these guidelines are not followed making the identification of links between unit tests and classes a time-consuming task. Thus, automatic approaches to recover such links are needed. In this paper a traceability recovery approach based on Data Flow Analysis (DFA) is presented. In particular, the approach retrieves as tested classes all the classes that affect the result of the last assert statement in each method of the unit test class. The accuracy of the proposed method has been empirically evaluated on two systems, an open source system and an industrial system. As a benchmark, we compare the accuracy of the DFA-based approach with the accuracy of the previously used traceability recovery approaches, namely Naming Convention (NC) and Last Call Before Assert (LCBA) that seem to provide the most accurate results. The results show that the proposed approach is the most accurate method demonstrating the effectiveness of DFA. However, the case study also highlights the limitations of the experimented traceability recovery approaches, showing that detecting the class under test cannot be fully automated and some issues are still under study.