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Invited Talks:

Hong Cheng, The Chinese University of Hong Kong

Identifying Bug Signatures Using Discriminative Graph Mining

Abstract: Bug localization has attracted a lot of attention recently. Most existing methods focus on pinpointing a single statement or function call which is very likely to contain bugs. Although such methods could be very accurate, it is usually very hard for developers to understand the context of the bug, given each bug location in isolation. In our work, we model software executions as graphs at two levels of granularity: methods and basic blocks. An individual node represents a method or basic block and an edge represents a method call, method return or transition (at the method or basic block granularity). Given a set of graphs of correct and faulty executions, we propose to extract the most discriminative subgraphs which contrast the program flow of correct and faulty executions. The extracted subgraphs not only pinpoint the bug, but also provide an informative context for understanding and fixing the bug. Different from traditional graph mining which mines a very large set of frequent subgraphs, we formulate subgraph mining as an optimization problem and directly generate the top-K most discriminative Subgraphs representing distinct locations which may contain bugs. Experimental results and case studies show that our proposed method is both effective and efficient to mine discriminative subgraphs for bug localization and context identification.

Bio: Dr. Hong Cheng is an Assistant Professor in the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong. She received her Ph.D. degree from University of Illinois at Urbana-Champaign in 2008. Dr. Cheng's research interests include data mining, database systems and machine learning. She also studies analyzing software code and execution using data mining approaches. She has published over 50 research papers in international conferences and journals, including SIGMOD, VLDB, SIGKDD, ICDE, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery from Data, and Data Mining and Knowledge Discovery, and received research paper awards at ICDE'07, SIGKDD'06 and SIGKDD'05. She is a finalist for the 2009 SIGKDD Doctoral Dissertation Award. Dr. Cheng is a recipient of the 2010 Vice-Chancellor's Exemplary Teaching Award at the Chinese University of Hong Kong.

Shi Han, Microsoft Research Asia

Software Analytics in Practice — Approaches and Experiences

Abstract: A huge wealth of various data exists in the software development process, and hidden in the data is information about the quality of software and services as well as the dynamics of software development. With various analytic and computing technologies, software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services. Software analytics is naturally tied with the software development practice mainly because (1) the data under study comes from real practice; (2) there are real problems to be answered using the data; (3) one of the success metrics of software analytics research is its influence and impact on the development practice. The process of transferring software analytics research results into practical use, a.k.a. technology transfer, is full of challenges, such as dealing with the scale and complexity of the real data, walking the last mile to build tools working well in practice instead of only being a demo or prototype, and effectively engaging the software practitioners to adopt the tools and provide feedback. At the Software Analytics group in Microsoft Research Asia, we are conducting research in software analytics; and we also collaborate extensively with product teams across Microsoft. In this talk, I will discuss some of the research projects in our group; and I will also use some case studies to share our approaches and experiences in technology transfer.

Bio: Shi Han is an Associate Researcher at the Software Analytics group of Microsoft Research Asia (MSRA). His research interests include data-driven software analysis, machine learning, and large-scale computing platform. Incorporating expertise from these domains, he has been pursuing research on performance analysis for large scale system software. Since 2009, he has been the key contributor to StackMine – a scalable stack trace mining platform for Windows performance debugging in the large. Before 2009, he was a key contributor to the algorithm design and implementation of the HMM-based East Asian character handwriting recognition engine in Microsoft Windows 7.