When debugging encounters artificial intelligence: state of the art and open challenges

Both software debugging and artificial intelligence techniques are hot topics in the current field of software engineering. Debugging techniques, which comprise fault localization and program repair, are an important part of the software development lifecycle for ensuring the quality of software systems. As the scale and complexity of software systems grow, developers intend to improve the effectiveness and efficiency of software debugging via artificial intelligence (artificial intelligence for software debugging, AI4SD). On the other hand, many artificial intelligence models are being integrated into safety-critical areas such as autonomous driving, image recognition, and audio processing, where software debugging is highly necessary and urgent (software debugging for artificial intelligence, SD4AI). An AI-enhanced debugging technique could assist in debugging AI systems more effectively, and a more robust and reliable AI approach could further guarantee and support debugging techniques. Therefore, it is important to take AI4SD and SD4AI into consideration comprehensively. In this paper, we want to show readers the path, the trend, and the potential that these two directions interact with each other. We select and review a total of 165 papers in AI4SD and SD4AI for answering three research questions, and further analyze opportunities and challenges as well as suggest future directions of this cross-cutting area.