Broadening Software Quality Assurance: From Improving Machine Learning Models to Making Software Accessible

Ifekhar Ahmed
Assistant Professor
Informatics
UC Irvine

Abstract:
Software will always have bugs, and as software continues to become a more and more pervasive part of our lives, software bugs will continue to affect more people than ever. Our confidence in software systems traditionally depends on exhaustive testing, but as systems get more complex, thorough testing becomes more challenging and sometimes infeasible. To build less error-prone systems, we must not only quickly and efficiently identify bugs through testing and verification but also identify factors associated with bugs to prevent them in the first place. Since software development is a complex process involving both people and technology, we need to examine socio-technical factors that affect code quality along with traditionally investigated technical factors. In this talk, I will present my research on how commit messages, a socio-technical factor in software projects, impact project quality. Another factor that we need to be aware of is the impact of different techniques used in quality assurance, especially the machine learning techniques which are used nowadays in almost all quality assurance approaches. I will present my research on identifying and fixing weaknesses in these techniques, particularly in defect prediction. Finally, I will present my research on improving software quality in terms of accessibility.

Bio:
Iftekhar Ahmed received his B.Sc. degree in Computer Science and Engineering from Shahjalal University of Science and Technology, Bangladesh. He received Ph.D. degree in Computer Science from Oregon State University. He is an Assistant Professor in the School of Information and Computer Sciences, University of California, Irvine, where he leads the Software Engineering & Testing Using Artificial Intelligence for Reliable Software (STAIRS) group. He is also a faculty member of the Institute for Software Research, UCI. His research interests include the field of software engineering, with a focus on combining testing, static analysis, socio-technical factors analysis, and machine learning approaches to help improve software quality under real-world conditions. His Ph.D. work on improving the effectiveness of mutation analysis for large-scale real-world software systems has helped to identify several bugs in the Linux kernel. The improvements resulting from his work have been incorporated into the Linux distributions, with more than 2 Billion instances running worldwide from mobile phones to data centers. His recent work on test smells detection is implemented as a plugin for PyCharm, a popular IDE for Python developed by JetBrains with more than 10 million developers using it worldwide. He is a member of the ACM, the ACM SIGSOFT, and the IEEE.

For more information, please visit his webpage at: http://www.iftekharahmed.info