Recently many information retrieval (IR)-based approaches have been proposed to help locate software defects automatically by using information from bug report contents. However, some bug reports that do not semantically related to the relevant code are not helpful to IR-based systems. Running an IR-based system on these reports may produce false positives. In this paper, we propose a classification model for classifying a bug report as either helpful or unhelpful using a LSTM-network. By filtering our unhelpful reports before running an IR-based bug locating system, our approach helps reduce false positives and improve the ranking performance. We test our model over 9,000 bug reports from three software projects. The evaluation result shows that our model helps improve a state-of-the-art IR-based system's ranking performance under a trade-off between the precision and the recall. Our comparison experiments show that the LSTM-network achieves the best trade-off between precision and recall than other classification models including CNN, multilayer perceptron, and a simple baseline approach that classifies a bug report based its length. In the situation that precision is more important than recall, our classification model helps for bug locating.

With the explosion of online articles, people are in desperate need of a more efficient way to filter, archive, and organize contents online. This paper presents the design and implementation of a cloud social media platform named Motif, which is a crowdsourcing reading platform for accessing interesting online information more efficiently. Motif is designed for readers to crowdsource and manage online information with a primary focus on articles. Readers' comments on articles can be shared with all or a selective group of users using Motif. The shared comments on an article may help users, who have not yet read this article, obtain an overall information of it efficiently so that they can decide whether to spend time on it or not. Motif is created for providing users with a friendly experience of discovering high-quality articles as well as managing their historical reading record efficiently.

The application of information retrieval techniques to search tasks in software engineering is made difficult by the lexical gap between search queries, usually expressed in natural language (e.g. English), and retrieved documents, usually expressed in code (e.g. programming languages). This is often the case in bug and feature location, community question answering, or more generally the communication between technical personnel and non-technical stake holders in a software project. In this paper, we propose bridging the lexical gap by projecting natural language statements and code snippets as meaning vectors in a shared representation space. In the proposed architecture, word embeddings are first trained on API documents, tutorials, and reference documents, and then aggregated in order to estimate semantic similarities between documents. Empirical evaluations show that the learned vector space embeddings lead to improvements in a previously explored bug localization task and a newly defined task of linking API documents to computer programming questions.

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and improve productivity. This paper introduces an adaptive ranking approach that leverages project knowledge through functional decomposition of source code, API descriptions of library components, the bug-fixing history, the code change history, and the file dependency graph. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. We evaluate the ranking system on six large scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the learning-to-rank approach outperforms three recent state-of-the-art methods. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70 percent of the bug reports in the Eclipse Platform and Tomcat projects.

When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files of a project with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and potentially could lead to a substantial increase in productivity. This paper introduces an adaptive ranking approach that leverages domain knowledge through functional decompositions of source code files into methods, API descriptions of library components used in the code, the bug-fixing history, and the code change history. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. We evaluated our system on six large scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the newly introduced learning-to-rank approach significantly outperforms two recent state-of-the-art methods in recommending relevant files for bug reports. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70% of the bug reports in the Eclipse Platform and Tomcat projects.