ChatGPT presents emerging opportunities in software development, yet its capabilities for understanding code remain largely understudied. This study aims to focus on the logical aspect of code comprehension of ChatGPT by examining its performance in detecting and fixing bugs. Our preliminary results suggest that ChatGPT seems to correct code in a different way than human logical steps.

Automated program generation (APG) is a concept of automatically making a computer program. Toward this goal, transferring automated program repair (APR) to APG can be considered. APR modifies the buggy input source code to pass all test cases. APG regards empty source code as initially failing all test cases, i.e., containing multiple bugs. Search-based APR repeatedly generates program variants and evaluates them. Many traditional APR systems evaluate the fitness of variants based on the number of passing test cases. However, when source code contains multiple bugs, this fitness function lacks the expressive power of variants. In this paper, we propose the application of a multi-objective genetic algorithm to APR in order to improve efficiency. We also propose a new crossover method that combines two variants with complementary test results, taking advantage of the high expressive power of multi-objective genetic algorithms for evaluation. We tested the effectiveness of the proposed method on competitive programming tasks. The obtained results showed significant differences in the number of successful trials and the required generation time.

Scripted testing and exploratory testing are widely adopted among developers. It is known that exploratory testing can detect bugs more efficiently than scripted testing. However, exploratory testing has the problem that it is difficult to agree with third parties on test results and test quality. In this study, we propose a novel testing approach to facilitate efficient testing and quality assurance. Furthermore, we propose a tool that records the details of testing activity and visualizes them in multiple ways. Our approach realizes both high efficiency like exploratory testing and high auditability like scripted testing. We interviewed 14 developers about our approach to refine it and make it practical. We describe how we reflected the feedback from the interview in our approach.

Automated program repair (in short, APR) has been attracting much attention. A variety of APR techniques have been proposed, and they have been evaluated with actual bugs in open source software. Currently, the authors are trying to introduce APR techniques to industrial software development (in short, ISD) to reduce development cost drastically. However, at this moment, there are no studies that report evaluations of APR techniques on ISD. In this paper, we report our ongoing application of APR techniques to ISD and discuss some barriers that we found on the application.

Much work has been done on automating regression testing for applications. But most of them focus on test execution. Little work has been done on automatically determining if a test case passes or fails. This decision is often made by comparing the results of executing test cases on a base version of the application and post-modification version of the application. If the two results match, the test case passes, otherwise fails. However, to the best of our knowledge, there is no regression testing method for automatically deciding pass/fail of dynamic Web applications which use JavaScript or CSS. We propose a method that automatically decides if a dynamic Web application passes a regression test case. The basic idea is to obtain a screenshot each time the GUI of the Web application (i.e. Web page) changes its state, and then compare each corresponding screenshot to see if they match. The evaluation results showed that the accuracy rate of our approach is high and our approach can be considered as fast enough for practical use.