On the Effectiveness of Unit Tests in Test-driven Development

Background: Writing unit tests is one of the primary activities in test-driven development. Yet, the existing reviews report few evidence supporting or refuting the effect of this development approach on test case quality. Lack of ability and skills of developers to produce sufficiently good test cases are also reported as limitations of applying test-driven development in industrial practice. Objective: We investigate the impact of test-driven development on the effectiveness of unit test cases compared to an incremental test last development in an industrial context. Method: We conducted an experiment in an industrial setting with 24 professionals. Professionals followed the two development approaches to implement the tasks. We measure unit test effectiveness in terms of mutation score. We also measure branch and method coverage of test suites to compare our results with the literature. Results: In terms of mutation score, we have found that the test cases written for a test-driven development task have a higher defect detection ability than test cases written for an incremental test-last development task. Subjects wrote test cases that cover more branches on a test-driven development task compared to the other task. However, test cases written for an incremental test-last development task cover more methods than those written for the second task. Conclusion: Our findings are different from previous studies conducted at academic settings. Professionals were able to perform more effective unit testing with test-driven development. Furthermore, we observe that the coverage measure preferred in academic studies reveal different aspects of a development approach. Our results need to be validated in larger industrial contexts.Istanbul Technical University Scientific Research Projects (MGA-2017-40712), and the Academy of Finland (Decision No. 278354)

How Effectively Is Defective Code Actually Tested?: An Analysis of JUnit Tests in Seven Open Source Systems

Background: Newspaper headlines still regularly report latent software defects. Such defects have often evaded testing for many years. It remains difficult to identify how well a system has been tested. It also remains difficult to assess how successful at finding defects particular tests are. Coverage and mutation testing are frequently used to asses test effectiveness. We look more deeply at the performance of commonly used JUnit testing by assessing how much JUnit testing was done and how effective that testing was at detecting defects in seven open source systems. Aim: We aim to identify whether defective code has been effectively tested by JUnit tests as non-defective code. We also aim to identify the characteristics of JUnit tests that are related to identifying defects. Methodology: We first extract the defects from seven open source projects using the SZZ algorithm. We match those defects with JUnit tests to identify the proportion of defects that were covered by JUnit tests. We also do the same for non-defective code. We then use Principal Component Analysis and machine learning to investigate the characteristics of JUnit tests that were successful in identifying defects. Results: Our findings suggest that most of the open source systems we investigated are under-tested. On average over 66% of defective methods were not linked to any JUnit tests. We show that the number of methods touched by a JUnit test is strongly related to that test uncovering a defect. Conclusion: More JUnit tests need to be produced for the seven open source systems that we investigate. JUnit tests need to be relatively sophisticated, in particular they should touch more than just one method during the test