In software maintenance, severity prediction on defect reports is an emerging issue obtaining research attention due to the considerable triaging cost. In the past research work, several text mining approaches have been proposed to predict the severity using advanced learning models. Although these approaches demonstrate the effectiveness of predicting the severity, they do not discuss the problem of how to find the indicators in good quality. In this paper, we discuss whether feature selection can benefit the severity prediction task with three commonly used feature selection schemes, Information Gain, Chi-Square, and Correlation Coefficient, based on the Multinomial Naive Bayes classification approach. We have conducted empirical experiments with four open-source components from Eclipse and Mozilla. The experimental results show that these three feature selection schemes can further improve the predication performance in over half the cases.

In software testing and maintenance activities, the observed faults and bugs are reported in bug report managing systems (BRMS) for further analysis and repair. According to the information provided by bug reports, developers need to find out the location of these faults and fix them. However, bug locating usually involves intensively browsing back and forth through bug reports and software code and thus incurs unpredictable cost of labor and time. Hence, establishing a robust model to efficiently and effectively locate and track faults is crucial to facilitate software testing and maintenance. In our observation, some related bug locations are tightly associated with the implicit links among source files. In this paper, we present an implicit social network model using PageRank to establish a social network graph with the extracted links. When a new bug report arrives, the prediction model provides users with likely bug locations according to the implicit social network graph constructed from the co-cited source files. The proposed approach has been implemented in real-world software archives and can effectively predict correct bug locations.

Maintaining traceability links among software artifacts is particularly important for many software engineering tasks. Even though automatic traceability link recovery tools are successful in identifying the semantic connections among software artifacts produced during software development, no existing traceability link management approach can effectively and automatically deal with software evolution. We propose a technique to automatically manage traceability link evolution and update the links in evolving software. Our novel technique, called incremental latent semantic indexing (iLSI), allows for the fast and low-cost LSI computation for the update of traceability links by analyzing the changes to software artifacts and by reusing the result from the previous LSI computation before the changes. We present our iLSI technique, and describe a complete automatic traceability link evolution management tool, TLEM, that is capable of interactively and quickly updating traceability links in the presence of evolving software artifacts. We report on our empirical evaluation with various experimental studies to assess the performance and usefulness of our approach.

Bug locating usually involves intensive search activities and incurs unpredictable cost of labor and time. An issue of information retrieval on bug locations is particularly addressed to facilitate identifying bugs from software code. In this paper, a novel bug retrieval approach with co-location shrinkage (CS) is proposed. The proposed approach has been implemented in open-source software projects collected from real-world repositories, and consistently improves the retrieval accuracy of a state-of-the-art Support Vector Machine (SVM) model.

Web catalog integration is an interesting problem in current digital content management. Past studies have shown that using a flattened structure with auxiliary information extracted from the source catalog can improve the integration results. However, the nature of a flattened structure ignores the hierarchical relationships, and thus the performance improvement of catalog integration may be reduced. In this paper, we propose an enhanced hierarchical catalog integration (EHCI) approach with conceptual thesauri extracted from the source catalog. The results show that our enhanced hierarchical integration approach effectively boosts the accuracy of hierarchical catalog integration.

The techniques of information visualization have been initially applied to the semantic Web to facilitate the presentation of data semantics. Currently, the semantic Web visualization interfaces are separately based on two distinctive frameworks, RDF and topic maps. Since the visualization interfaces based on each framework only present a partial view of semantic Web, an integration mechanism is needed to demonstrate them together for the panorama of semantic Web. In this paper, we present an integrated semantic Web interactive visualization environment (ISWIVE) to incorporate the topic features from topic maps into RDF. Both the detailed resource descriptions and the overall topic relationship can be clearly visualized in ISWIVE. Besides, an interactive local viewer and visual query interface facilitate browsing and searching over the semantic Web resources.