Abstract-Locating bugs is important, difficult, and expensive, particularly for large-scale systems. To address this, natural language information retrieval techniques are increasingly being used to suggest potential faulty source files given bug reports. While these techniques are very scalable, in practice their effectiveness remains low in accurately localizing bugs to a small number of files. Our key insight is that structured information retrieval based on code constructs, such as class and method names, enables more accurate bug localization. We present BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available. We build BLUiR on a proven, open source IR toolkit that anyone can use. Our work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice. We evaluate BLUiR on four open source projects with approximately 3,400 bugs. Results show that BLUiR matches or outperforms a current state-of-theart tool across applications considered, even when BLUiR does not use bug similarity data used by the other tool.

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Regression testing is widely used in practice for validating program changes. However, running large regression suites can be costly. Researchers have developed several techniques for prioritizing tests such that the higher-priority tests have a higher likelihood of finding bugs. A vast majority of these techniques are based on dynamic analysis, which can be precise but can also have significant overhead (e.g., for program instrumentation and test-coverage collection). We introduce a new approach, REPiR, to address the problem of regression test prioritization by reducing it to a standard Information Retrieval problem such that the differences between two program versions form the query and the tests constitute the document collection. REPiR does not require any dynamic profiling or static program analysis. As an enabling technology we leverage the open-source IR toolkit Indri. An empirical evaluation using eight open-source Java projects shows that REPiR is computationally efficient and performs better than existing (dynamic or static) techniques for the majority of subject systems.

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Despite significant advances in automatic program repair (APR) techniques over the past decade, practical deployment remains an elusive goal. One of the important challenges in this regard is the general inability of current APR techniques to produce patches that require edits in multiple locations, i.e., multi-hunk patches. In this work, we present a novel APR technique that generalizes singlehunk repair techniques to include an important class of multi-hunk bugs, namely bugs that may require applying a substantially similar patch at a number of locations. We term such sets of repair locations as evolutionary siblings -similar looking code, instantiated in similar contexts, that are expected to undergo similar changes. At the heart of our proposed method is an analysis to accurately identify a set of evolutionary siblings, for a given bug. This analysis leverages three distinct sources of information, namely the test-suite spectrum, a novel code similarity analysis, and the revision history of the project. The discovered siblings are then simultaneously repaired in a similar fashion. We instantiate this technique in a tool called Hercules and demonstrate that it is able to correctly fix 49 bugs in the Defects4J dataset, the highest of any individual APR technique to date. This includes 15 multi-hunk bugs and overall 13 bugs which have not been fixed by any other technique so far. CCS CONCEPTS• Software and its engineering → Software testing and debugging; KEYWORDS Automatic Program Repair, Machine Learning, OOP, Multi-hunk patches, Code Similarity † This work was done when the author was an intern at Fujitsu Laboratories of America. 1 We use evolution as a metaphor for the environment, i.e., context, of a piece of code, in addition to the changes it undergoes over its lifetime.

Stack Overflow is a highly successful question-answering website in the programming community, which not only provide quick solutions to programmers' questions but also is considered as a large repository of valuable software engineering knowledge. However, despite having a very engaged and active user community, Stack Overflow currently has more than 300K unanswered questions. In this paper, we perform an initial investigation to understand why these questions remain unanswered by applying a combination of statistical and data mining techniques. Our preliminary results indicate that although there are some topics that were never answered, most questions remained unanswered because they apparently are of little interest to the user community.

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