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The interplay of multiple objects in object-oriented programming often follows specific protocols, for example certain orders of method calls and/or control structure constraints among them that are parts of the intended object usages. Unfortunately, the information is not always documented. That creates long learning curve, and importantly, leads to subtle problems due to the misuse of objects.In this paper, we propose GrouMiner, a novel graph-based approach for mining the usage patterns of one or multiple objects. GrouMiner approach includes a graph-based representation for multiple object usages, a pattern mining algorithm, and an anomaly detection technique that are efficient, accurate, and resilient to software changes. Our experiments on several real-world programs show that our prototype is able to find useful usage patterns with multiple objects and control structures, and to translate them into user-friendly code skeletons to assist developers in programming. It could also detect the usage anomalies that caused yet undiscovered defects and code smells in those programs.

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Locating buggy code is a time-consuming task in software development. Given a new bug report, developers must search through a large number of files in a project to locate buggy code. We propose BugScout, an automated approach to help developers reduce such efforts by narrowing the search space of buggy files when they are assigned to address a bug report. BugScout assumes that the textual contents of a bug report and that of its corresponding source code share some technical aspects of the system which can be used for locating buggy source files given a new bug report. We develop a specialized topic model that represents those technical aspects as topics in the textual contents of bug reports and source files, and correlates bug reports and corresponding buggy files via their shared topics. Our evaluation shows that BugScout can recommend buggy files correctly up to 45% of the cases with a recommended ranked list of 10 files.