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.

BackgroundMesenchymal stem cells (MSCs) can differentiate into chondroblasts, adipocytes, or osteoblasts under appropriate stimulation. Mechano-growth factor (MGF) reportedly displays a neuroprotective effect in cerebral regions that were exposed to ischemia and is expressed in stromal cells of the eutopic endometrium and in glandular cells of the ectopic endometrium.ResultsThis study sought to understand the potential involvement of phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT) in MGF-induced growth of rabbit MSCs (rMSCs). We applied various concentrations of MGF to cultured rMSCs and observed the growth rate of the cells, the changes in the phosphorylation state of AKT and mammalian target of rapamycin (mTOR), and the expression levels of alkaline phosphatase and osteocalcin. We found that the growth and osteogenic differentiation of MGF-induced rMSCs were promoted primarily by phosphorylated AKT, and that this phosphorylation, as well mTOR phosphorylation, was mediated by the MGF receptor.ConclusionOur study suggests that MGF promotes the growth and osteogenic differentiation of rMSCs primarily through the PI3K/AKT pathway.

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.

Bug localization represents one of the most expensive, as well as time-consuming, activities during software maintenance and evolution. To alleviate the workload of developers, numerous methods have been proposed to automate this process and narrow down the scope of reviewing buggy files. In this paper, we present a novel buggy source file localization approach, using the information from both the bug reports and the source files. We leverage the part-of-speech features of bug reports and the invocation relationship among source files. We also integrate an adaptive technique to further optimize the performance of our approach. The adaptive technique discriminates Top 1 and Top N recommendations for a given bug report and consists of two modules. One module is to maximize the accuracy of the first recommended file, and the other one aims at improving the accuracy of the fixed defect file list. We evaluate our approach on six large-scale open source projects, i.e., ASpectJ, Eclipse, SWT, Zxing, Birt and Tomcat. Compared to the previous work, empirical results show that our approach can improve the overall prediction performance in all of these cases. Particularly, in terms of the Top 1 recommendation accuracy, our approach achieves an enhancement from 22.73% to 39.86% for ASpectJ, from 24.36% to 30.76% for Eclipse, from 31.63% to 46.94% for SWT, from 40% to 55% for ZXing, from 7.97% to 21.99% for Birt, and from 33.37% to 38.90% for Tomcat.

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