Automatic Software Repair Professors and Supervisors

Xiaoyin WangView profile

Associate Professor

National and Kapodistrian University of AthensGreece

Xiaoyin Wang is an Associate Professor in the Department of Computer Science at the University of Texas at San Antonio, specializing in software testing, privacy, and program analysis. Her research focuses on enhancing software quality through innovative approaches to build systems, virtual reality testing, and privacy policy enforcement. She earned her PhD from Peking University in 2012 and completed postdoctoral work at UC Berkeley. Her research interests span Software Testing and Analysis , Software Privacy , Build Systems , and Virtual Reality Testing . She develops techniques for detecting privacy leaks in mobile apps, optimizing build scripts, and testing immersive applications. Her work bridges program analysis with practical software engineering challenges, particularly in security-critical contexts. Recent publications reveal strong trends in Virtual Reality testing frameworks (e.g., VRGuide, VRTest), automated build diagnostics (PExReport, HireBuild), and privacy policy verification (GUILeak, DAISY). Her research increasingly integrates machine learning for bug prediction and test oracle generation, with significant contributions to augmented reality testing and cross-project compatibility analysis. President's Research Achievement Award (UTSA, 2019) NSF CAREER Award (2019) for build script analysis Distinguished PhD Dissertation Awards from CCF (2013) and Peking University (2012) MSRA Fellowship (2009) Wang actively advises PhD students including Rodney Rodriguez (defended 2022, now at Accenture) and Xueling Zhang (defended 2021, now Assistant Professor at RIT). She leads NSF-funded projects including CAREER: Analysis and Repair of Build Scripts for DevOps and EAGER: Tracing Privacy-Policy Statements into Code . Her service includes chairing ICSE 2019 proceedings and serving on ASE/ICSE/ISSTA program committees. Current work focuses on security analysis for AR applications and automated privacy compliance verification.

Official University Profile

Yintong HuoView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Yintong Huo is an Assistant Professor at the School of Computing & Information Systems, Singapore Management University (SMU), where he leads research in intelligent software engineering. He received his PhD from The Chinese University of Hong Kong (CUHK) in 2024 under Prof. Michael R. Lyu and holds a Bachelor's degree from the University of Electronic Science and Technology of China. His research focuses on empowering AI models (particularly LLMs) for software development, testing, and operations, with two flagship projects: LogPAI (open-source AI platform for automated log analysis) and WebPAI (multimodal intelligence for automatic webpage development). His work spans log analysis, code intelligence, UI generation from prototypes, and configuration diagnostics. Huo's publication record shows strong trends in leveraging multimodal LLMs for practical software engineering challenges, with recent work on interactive webpage generation (Interaction2Code), configuration logging (ConfLogger), and log parsing (LILAC). His research bridges theoretical AI advancements with real-world system reliability needs. ICSE Distinguished Reviewer Award (2025) ISSRE Distinguished Reviewer Award (2024) IEEE Open Software Services Award (2022) ACM SIGSOFT CAPS Travel Grants National Scholarship (2019) Huo actively supervises PhD students (including Shi Ying Chang and Dan Huang) and research engineers. His lab has secured funding for multiple projects including WebPAI and LogPAI. He serves on program committees for major conferences (ASE, ICSE, FSE) and reviews for top journals. Current projects include dynamic webpage generation and configuration diagnostics, with ongoing work on small language models for logging systems. Huo leads the LogPAI and WebPAI research groups, developing open-source tools for automated log analysis and multimodal UI code generation. The LogPAI project has garnered over 3,000 GitHub stars and 70,000 downloads. His team collaborates with industry partners on AIOps challenges and is expanding into configuration diagnostics through the ConfLogger project.

Official University Profile

Ajitha RajanView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Ajitha Rajan is a Professor (Personal Chair of Software Testing & Verification) at the School of Informatics, University of Edinburgh. She joined the university in December 2012 as a Reader (equivalent to Associate Professor in American terms) and was promoted to Professor in 2024. Prior to her position at Edinburgh, she held postdoctoral positions at Oxford University and Laboratoire d'Informatique de Grenoble in France. She earned her PhD in Computer Science from the University of Minnesota in August 2009 under Professor Mats Heimdahl. Her research focuses on two primary directions: Automated Software Testing (including test input generation, test oracles, and coverage measurement) and Biomedical AI (particularly cancer survival models and interpretability for biological sequences and medical images). Her work has applications in safety-critical systems, blockchains, embedded systems, and medical diagnostics. She has made significant contributions to explainable AI for healthcare applications, especially in lung cancer detection and cancer survival analysis. Her recent publications demonstrate a strong trend toward interdisciplinary research at the intersection of software engineering and biomedical applications. She has numerous publications in top venues including ICSE, ASE, and healthcare-focused conferences. Her work increasingly focuses on making AI systems more interpretable and trustworthy, particularly in medical contexts where model decisions can have life-or-death consequences. ACM SIGSOFT Distinguished Reviewer Award, ISSTA 2025 Best Paper Award at ICHI 2025 Promoted to Professor (Chair in Software Testing & Verification) 2024 SICSA Best Supervisor Award 2024 ACM Distinguished Paper Award 2008 Professor Rajan actively supervises PhD students in both software testing and biomedical AI domains. She leads several funded projects including MANIFEST (a cancer immunotherapy response research platform), a Huawei Joint Lab project on RobustCheck, a Royal Society Industry Fellowship on AutoTest, and the KATY project on clinical knowledge for personalized medicine. Her research group includes current PhD students working on explainable AI for medical image analysis, scenario-based testing for autonomous driving, and protein design applications.

Official University Profile