To facilitate software quality assurance, defect prediction metrics, such as source code metrics, change churns, and the number of previous defects, have been actively studied. Despite the common understanding that developer behavioral interaction patterns can affect software quality, these widely used defect prediction metrics do not consider developer behavior. We therefore propose micro interaction metrics (MIMs), which are metrics that leverage developer interaction information. The developer interactions, such as file editing and browsing events in task sessions, are captured and stored as information by Mylyn, an Eclipse plug-in. Our experimental evaluation demonstrates that MIMs significantly improve overall defect prediction accuracy when combined with existing software measures, perform well in a cost-effective manner, and provide intuitive feedback that enables developers to recognize their own inefficient behaviors during software development. 

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Adherence to coding conventions during the code production stage of software development is essential. Benefits include enabling programmers to quickly understand the context of shared code, communicate with one another in a consistent manner, and easily maintain the source code at low costs. In reality, however, programmers tend to doubt or ignore the degree to which the quality of their code is affected by adherence to these guidelines. This paper addresses research questions such as “Do violations of coding conventions affect the readability of the produced code?”, “What kinds of coding violations reduce code readability?”, and “How much do variable factors such as developer experience, project size, team size, and project maturity influence coding violations?” To respond to these research questions, we explored 210 open-source Java projects with 117 coding conventions from the Sun standard checklist. We believe our findings and the analysis approach used in the paper will encourage programmers and QA managers to develop their own customized and effective coding style guidelines.

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Many existing warning prioritization techniques seek to reorder the static analysis warnings such that true positives are provided first. However, excessive amount of time is required therein to investigate and fix prioritized warnings because some are not actually true positives or are irrelevant to the code context and topic. In this paper, we propose a warning prioritization technique that reflects various latent topics from bug-related code blocks. Our main aim is to build a prioritization model that comprises separate warning priorities depending on the topic of the change sets to identify the number of true positive warnings. For the performance evaluation of the proposed model, we employ a performance metric called warning detection rate, widely used in many warning prioritization studies, and compare the proposed model with other competitive techniques. Additionally, the effectiveness of our model is verified via the application of our technique to eight industrial projects of a real global company.

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