Is the Stack Distance Between Test Case and Method Correlated With Test Effectiveness?

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.Comment: EASE 201

Goal-Oriented Mutation Testing with Focal Methods

Mutation testing is the state-of-the-art technique for assessing the fault-detection capacity of a test suite. Unfortunately, mutation testing consumes enormous computing resources because it runs the whole test suite for each and every injected mutant. In this paper we explore fine-grained traceability links at method level (named focal methods), to reduce the execution time of mutation testing and to verify the quality of the test cases for each individual method, instead of the usually verified overall test suite quality. Validation of our approach on the open source Apache Ant project shows a speed-up of 573.5x for the mutants located in focal methods with a quality score of 80%.Comment: A-TEST 201

Revisiting the Relationship Between Fault Detection,Test Adequacy Criteria, and Test Set Size

The research community has long recognized a complex interrelationship between test set size, test adequacy criteria, and test effectiveness in terms of fault detection. However, there is substantial confusion about the role and importance of controlling for test set size when assessing and comparing test adequacy criteria. This paper makes the following contributions: (1) A review of contradictory analyses of the relationship between fault detection, test suite size, and test adequacy criteria. Specifically, this paper addresses the supposed contradiction of prior work and explains why test suite size is neither a confounding variable, as previously suggested,nor an independent variable that should be experimentally manipulated. (2) An explication and discussion of the experimental design and sampling strategies of prior work, together with a discussion of conceptual and statistical problems, and specific guidelines for future work. (3) A methodology for comparing test-adequacy criteria on an equal basis, which accounts for test suite size by treating it as a covariate. (4) An empirical evaluation that compares the effectiveness of coverage-based and mutation-based testing to one another and random testing. Additionally, this paper proposes probabilistic coupling, a methodology for approximating the representativeness of a set of test goals for a given set of real fault