Automated test case generation has proven to be useful to reduce the usually high expenses of software testing. However, several studies have also noted the skepticism of testers regarding the comprehension of generated test suites when compared to manually designed ones. This fact suggests that involving testers in the test generation process could be helpful to increase their acceptance of automatically-produced test suites. In this paper, we propose incorporating interactive readability assessments made by a tester into EvoSuite, a widely-known evolutionary test generation tool. Our approach,InterEvo-TR, interacts with the tester at different moments during the search and shows different test cases covering the same coverage target for their subjective evaluation. The design of such an interactive approach involves a schedule of interaction, a method to diversify the selected targets, a plan to save and handle the readability values, and some mechanisms to customize the level of engagement in the revision, among other aspects. To analyze the potential and practicability of our proposal, we conduct a controlled experiment in which 39 participants, including academics, professional developers, and student collaborators, interact withInterEvo-TR. Our results show that the strategy to select and present intermediate results is effective for the purpose of readability assessment. Furthermore, the participants’ actions and responses to a questionnaire allowed us to analyze the aspects influencing test code readability and the benefits and limitations of an interactive approach in the context of test case generation, paving the way for future developments based on interactivity.

Abstract Software testing is a complex and costly stage during the software development lifecycle. Nowadays, there is a wide variety of solutions to reduce testing costs and improve test quality. Focussing on test case generation, Dynamic Symbolic Execution (DSE) is used to generate tests with good structural coverage. Regarding test suite evaluation, Mutation Testing (MT) assesses the detection capability of the test cases by introducing minor localised changes that resemble real faults. DSE is however known to produce tests that do not have good mutation detection capabilities: in this paper, the authors set out to solve this by combining DSE and MT into a new family of approaches that the authors call Mutation‐Inspired Symbolic Execution (MISE). First, this known result on a set of open source programs is confirmed: DSE by itself is not good at killing mutants, detecting only 59.9% out of all mutants. The authors show that a direct combination of DSE and MT (naive MISE) can produce better results, detecting up to 16% more mutants depending on the programme, though at a high computational cost. To reduce these costs, the authors set out a roadmap for more efficient versions of MISE, gaining its advantages while avoiding a large part of its additional costs.

Test generation is a costly but necessary testing activity to increase the quality of software projects. Automated testing tools based on evolutionary computation principles constitute an appealing modern approach to support testing tasks. However, these tools still find difficulties to detect certain types of plausible faults in real-world projects. Besides, recent studies have shown that, in general, automatically-generated tests do not resemble those manually written and, consequently, testers are reluctant to adopt them. We observe two key issues, namely the opacity of the process and the lack of cooperation with the tester, currently hampering the acceptance of automated results. Based on these findings, we explore in this paper how the interaction between current tools and expert testers would help address the test case generation problem. More specifically, we identify a number of interaction opportunities related to the object-oriented test case design driven to boost their readability and detection power. Using EvoSuite as base implementation, we present a proof of concept focused on the possibility to integrate readability assessment of the most promising test suites into a genetic algorithm.