Test code evolution and mutation testing

In this issue, we are pleased to present two papers on test code evolution and mutation testing, respectively.

The first paper, “Towards automatically identifying the co-change of production and test code” by Yuan Huang, Zhicao Tang, Xiangping Chen and Xiaocong Zhou, presents a method named Jtup that uses machine learning to identify the cochange of production and test code. When a developer makes modifications to a class in production code, Jtup analyses the modified class and determines whether its corresponding test class needs to be modified as well. For machine learning, Jtup incorporates three types of features (code change features, code complexity features and code semantic features). The experimental results show the superior performance of Jtup in both within-project and multiclassification settings, surpassing multiple competing methods (Recommended by Wing Kwong Chan).

The second paper, “A new perspective on the competent programmer hypothesis through the reproduction of real faults with repeated mutations” by Zaheed Ahmed, Eike Schwass, Steffen Herbold, Fabian Trautsch and Jens Grabowski, presents a study of the competent programmer hypothesis based on the ability to reproduce faults through mutation operators. In contrast, previous work only considered how many tokens are changed by bugs or manually compared mutations with faults. The authors reframe the problem of transforming a correct into a buggy AST as a path search problem, where each step of a path is a mutation. The study results support the competent programmer hypothesis and also show that mutation operators are often not in line with the slight differences in correct code introduced by developers (Recommended by Marcio Delamaro).

We hope that these papers will inspire further research in related directions.