The processing priorities for software bug reports are important for software maintenance. Predicting the priorities for bug reports is the subject of many software engineering studies. This study proposes a priority prediction method that uses comment intensiveness features and a Synthetic Minority Over-sampling Technique (SMOTE)-based data balancing scheme. Experiments use datasets for three open-source projects: Eclipse, Mozilla and OpenOffice. The effectiveness of the proposed approach is determined using five classification models: Multinomial Naïve Bayes, Support Vector Machines, Random Forest, Extra Trees and eXtreme Gradient Boosting. The results show that the CIS-SMOTE-based models achieve 0.6078 Precision, 0.4927 Recall, 0.4465 F1-score and 0.7836 Accuracy in priority perdition. The results also show that CIS-SMOTE-RF, CIS-SMOTE-ET and CIS-SMOTE-XGB outperform two advanced priority prediction approaches, eApp and cPur, in terms of all performance measures.

In recent years, Android has become one of the most popular mobile operating systems.Software testing of Android applications becomes a very important issue.In the past research, many studies focus on functional testing.Recently, some studies start to discuss the layout anomaly issue.However, these schemes investigate few types of layout anomalies.Moreover, there may exist underlying platform constraints.In this paper, a detection tool LAD (Layout Anomaly Detector) is proposed.LAD considers the anomaly types studied in the previous work with a newly proposed anomaly type for scale maladaptation of components and text.LAD also supports testing scripts.We have conducted empirical experiments with four real apps.The experimental results show that LAD can effectively detect the layout anomalies of these apps.

Android GUI testing is an important research field to maintain software quality of Android apps. Although many GUI testing schemes have been investigated in the past, the discussion of exploration event scheduling has not been investigated comprehensively for the event replay process. Based on the component priority relationship, this study proposes a novel exploration event scheduling scheme called CPR to reduce the number of the testing events in the replay process. Compared with the breadth-first traversal scheme, the proposed CPR scheme can reduce up to 62% of testing events for achieving the same component coverage, and up to 69% of testing events for layout traversal. Compared with the depth-first traversal scheme, CPR can reduce up to 15% of testing events for achieving the same component coverage, and up to 42% of testing events for layout traversal. With respect to the testing time, CPR can achieve the best performance for most of the AUTs in the empirical study. The results of the empirical experiments show that the proposed CPR scheduling scheme can have the benefits in improving the testing performance.

Android is currently the most widely used operating system for mobile devices. GUI testing for Android applications becomes an important research area in which many studies have been conducted. The past studies show that testing a complicated GUI design may need a large number of test cases which increases exponentially due to the complexity of the GUI. Developers then need to spend a large amount of testing time in executing the test cases to explore the potential software defects. Unfortunately, the testing efficiency issue has not been comprehensively discussed in related studies. In this paper, we describe a parallel GUI testing platform called PATS (Parallel Android Testing System) which performs GUI testing based on a master-slave model. In PATS, the application under test is analyzed dynamically under the cooperation of the master and the slaves. Since the test cases are also generated in parallel at the runtime, the testing efficiency can be improved. We have implemented a prototype and conducted experiments with Android apps. The experimental results show that PATS can effectively improve the testing time with 18.87-35.78% performance improvements.

Processing bug reports plays an important role for software maintenance. Recently, the issue of detecting duplicate bug reports has been noticed due to their considerable appearances. In the past, many NLP-based detection schemes have been proposed. However, the cluster-level correlation relationships are not extensively considered in the past studies. In this paper, we present an improved detection scheme using cluster weighting to enhance the detection performance of a previous SVM-based method. We have conducted empirical studies with three open source software projects, Apache, ArgoUML, and SVN. Compared with the original SVM-based method, the proposed SVM-TC scheme can achieve 2.83-16.32% improvements of the top-5 recall rates in three projects.

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.

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.

Bluetooth is a prominent technology to construct a wireless personal area network. Recently, many scatternet formation schemes have been proposed to connect piconets and form a scatternet. Past studies have pointed out that appropriate role selection can improve the network performance and reduce the communication delay. However, previous schemes rarely address the node mobility issue. In this paper, we propose a scatternet formation scheme based on fuzzy logic called FLSF with considerations on two properties of Bluetooth devices, the remaining power and the mobility, to build a scatternet with high stability. The simulation results show that the FLSF scatternet has a longer lifetime and is comparatively more stable. We believe that FLSF can be practiced in real Bluetooth applications to construct a large-scale WPAN.

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.