Misclassification of bug reports inevitably sacrifices the performance of bug prediction models. Manual examinations can help reduce the noise but bring a heavy burden for developers instead. In this paper, we propose a hybrid approach by combining both text mining and data mining techniques of bug report data to automate the prediction process. The first stage leverages text mining techniques to analyze the summary parts of bug reports and classifies them into three levels of probability. The extracted features and some other structured features of bug reports are then fed into the machine learner in the second stage. Data grafting techniques are employed to bridge the two stages. Comparative experiments with previous studies on the same data-three large-scale open source projectsconsistently achieve a reasonable enhancement (from 77.4% to 81.7%, 73.9% to 80.2% and 87.4% to 93.7%, respectively) over their best results in terms of overall performance. Additional comparative empirical experiments on other two popular open source repositories confirm the findings and demonstrate the benefits of our approach.

Abstract-Application Programming Interface (API) documents represent one of the most important references for API users. However, it is frequently reported that the documentation is inconsistent with the source code and deviates from the API itself. Such inconsistencies in the documents inevitably confuse the API users hampering considerably their API comprehension and the quality of software built from such APIs. In this paper, we propose an automated approach to detect defects of API documents by leveraging techniques from program comprehension and natural language processing. Particularly, we focus on the directives of the API documents which are related to parameter constraints and exception throwing declarations. A first-order logic based constraint solver is employed to detect such defects based on the obtained analysis results. We evaluate our approach on parts of well documented JDK 1.8 APIs. Experiment results show that, out of around 2000 API usage constraints, our approach can detect 1146 defective document directives, with a precision rate of 81.6%, and a recall rate of 82.0%, which demonstrates its practical feasibility.

Bug reports represent an important information source for software construction. Misclassification of these reports inevitably introduces bias. Manual examinations can help reduce the noise, but bring a heavy burden for developers instead. In this paper, we propose a multi‐stage approach by combining both text mining and data mining techniques to automate the prediction process. The first stage leverages text mining techniques to analyze the summary parts of bug reports and classifies them into three levels of probability. The extracted features and some other structured features of bug reports are then fed into the machine learner in the second stage. Data grafting techniques are employed to bridge the two stages. Comparative experiments with previous studies on the same data—three large‐scale open‐source projects—consistently achieve a reasonable enhancement (from 77.4% to 81.7%, 76.1% to 81.6%, and 87.4% to 93.7%, respectively) over their best results in terms of overall performance. Additional comparative empirical experiments on other seven popular open‐source systems confirm the findings. Moreover, based on the data obtained, we also empirically studied the impact relation between the underlying classifiers and various other properties of the combined model. A prototypical recommender system has been developed to demonstrate the applicability of our approach. Copyright © 2016 John Wiley & Sons, Ltd.