Sepehr Hashtroudi

Jiho Shin, Sepehr Hashtroudi, Hadi Hemmati et al.

Recently, deep learning-based test case generation approaches have been proposed to automate the generation of unit test cases. In this study, we leverage Transformer-based code models to generate unit tests with the help of Domain Adaptation (DA) at a project level. Specifically, we use CodeT5, a relatively small language model trained on source code data, and fine-tune it on the test generation task. Then, we apply domain adaptation to each target project data to learn project-specific knowledge (project-level DA). We use the Methods2test dataset to fine-tune CodeT5 for the test generation task and the Defects4j dataset for project-level domain adaptation and evaluation. We compare our approach with (a) CodeT5 fine-tuned on the test generation without DA, (b) the A3Test tool, and (c) GPT-4 on five projects from the Defects4j dataset. The results show that tests generated using DA can increase the line coverage by 18.62%, 19.88%, and 18.02% and mutation score by 16.45%, 16.01%, and 12.99% compared to the above (a), (b), and (c) baselines, respectively. The overall results show consistent improvements in metrics such as parse rate, compile rate, BLEU, and CodeBLEU. In addition, we show that our approach can be seen as a complementary solution alongside existing search-based test generation tools such as EvoSuite, to increase the overall coverage and mutation scores with an average of 34.42% and 6.8%, for line coverage and mutation score, respectively.

Nima Miryeganeh, Sepehr Hashtroudi, Hadi Hemmati

Fault Localization (FL) is an important first step in software debugging and is mostly manual in the current practice. Many methods have been proposed over years to automate the FL process, including information retrieval (IR)-based techniques. These methods localize the fault based on the similarity of the reported bug report and the source code. Newer variations of IR-based FL (IRFL) techniques also look into the history of bug reports and leverage them during the localization. However, all existing IRFL techniques limit themselves to the current project's data (local data). In this study, we introduce Globug, which is an IRFL framework consisting of methods that use models pre-trained on the global data (extracted from open-source benchmark projects). In Globug, we investigate two heuristics: a) the effect of global data on a state-of-the-art IR-FL technique, namely BugLocator, and b) the application of a Word Embedding technique (Doc2Vec) together with global data. Our large scale experiment on 51 software projects shows that using global data improves BugLocator on average 6.6% and 4.8% in terms of MRR (Mean Reciprocal Rank) and MAP (Mean Average Precision), with over 14% in a majority (64% and 54% in terms of MRR and MAP, respectively) of the cases. This amount of improvement is significant compared to the improvement rates that five other state-of-the-art IRFL tools provide over BugLocator. In addition, training the models globally is a one-time offline task with no overhead on BugLocator's run-time fault localization. Our study, however, shows that a Word Embedding-based global solution did not further improve the results.