With the increasing number of software bugs, bug fixing plays an important role in software development and maintenance. To improve the efficiency of bug resolution, developers utilize bug reports to resolve given bugs. Especially, bug triagers usually depend on bugs' descriptions to suggest priority levels for reported bugs. However, manual priority assignment is a time-consuming and cumbersome task. To resolve this problem, recent studies have proposed many approaches to automatically predict the priority levels for the reported bugs. Unfortunately, these approaches still face two challenges that include words' nonconsecutive semantics in bug reports and the imbalanced data. In this article, we propose a novel approach that graph convolutional networks (GCN) based on weighted loss function to perform the priority prediction for bug reports. For the first challenge, we build a heterogeneous text graph for bug reports and apply GCN to extract words' semantics in bug reports. For the second challenge, we construct a weighted loss function in the training phase. We conduct the priority prediction on four open-source projects, including Mozilla, Eclipse, Netbeans, and GNU compiler collection. Experimental results show that our method outperforms two baseline approaches in terms of the F-measure by weighted average of 13.22%.

We present 127 new transit light curves for 39 hot Jupiter systems, obtained over the span of 5 yr by two ground-based telescopes. A homogeneous analysis of these newly collected light curves together with archived spectroscopic, photometric, and Doppler velocimetric data using EXOFASTv2 leads to a significant improvement in the physical and orbital parameters of each system. All of our stellar radii are constrained to accuracies of better than 3%. The planetary radii for 37 of our 39 targets are determined to accuracies of better than 5%. Compared to our results, the literature eccentricities are preferentially overestimated due to the Lucy–Sweeney bias. Our new photometric observations therefore allow for significant improvement in the orbital ephemerides of each system. Our correction of the future transit window amounts to a change exceeding 10 minutes for 10 targets at the time of James Webb Space Telescope's launch, including a 72 minutes change for WASP-56. The measured transit midtimes for both literature light curves and our new photometry show no significant deviations from the updated linear ephemerides, ruling out in each system the presence of companion planets with masses greater than 0.39–5.0 M _⊕, 1.23–14.36 M _⊕, 1.65–21.18 M _⊕, and 0.69–6.75 M _⊕ near the 1:2, 2:3, 3:2, and 2:1 resonances with the hot Jupiters, respectively, at a confidence level of ±1σ. The absence of resonant companion planets in the hot Jupiter systems is inconsistent with the conventional expectation from disk migration.

The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-in-time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can provide timely feedback by determining whether a new code commit will introduce defects into the apps. As defect-prediction performance usually relies on the quality of the data representation and the used classification model, in this work, we propose a model, called Simplified Deep Forest (SDF), to conduct JIT defect prediction for Android mobile apps. SDF modifies a state-of-the-art deep forest model by removing the multigrained scanning operation that is designed for data with a high-dimensional feature space. It uses a cascade structure with ensemble forests for representation learning and classification. We conduct experiments on 10 Android mobile apps and experimental results show that SDF performs significantly better than comparative methods in terms of 3 performance indicators.

Just-in-time (JIT) bug prediction is an effective quality assurance activity that identifies whether a code commit will introduce bugs into the mobile app, aiming to provide prompt feedback to practitioners for priority review. Since collecting sufficient labeled bug data is not always feasible for some mobile apps, one possible approach is to leverage cross-app models. In this work, we propose a new cross-triplet deep feature embedding method, called CDFE, for cross-app JIT bug prediction task. The CDFE method incorporates a state-of-the-art cross-triplet loss function into a deep neural network to learn high-level feature representation for the cross-app data. This loss function adapts to the cross-app feature learning task and aims to learn a new feature space to shorten the distance of commit instances with the same label and enlarge the distance of commit instances with different labels. In addition, this loss function assigns higher weights to losses caused by cross-app instance pairs than that by intra-app instance pairs, aiming to narrow the discrepancy of cross-app bug data. We evaluate our CDFE method on a benchmark bug dataset from 19 mobile apps with two effort-aware indicators. The experimental results on 342 cross-app pairs show that our proposed CDFE method performs better than 14 baseline methods.

Fault localization aims at developing an effective methodology identifying suspicious statements potentially responsible for program failures. The spectrum-based fault localization is the widely used methodology by analyzing the statistical coincidences viewed from the spectrum to evaluate the suspiciousness of each statement of being faulty. However, just analyzing statistical coincidences in the coverage information perspective and without combining diverse amount of information may restrict fault localization effectiveness. Thus, this article proposes feature-based fault localization ( Feature-FL ): A family fault localization methodology of feature-based metrics by combining the feature diversity from the view of program features into suspiciousness evaluation. Specifically, Feature-FL defines a concept of branching execution probability to abstract program behaviors as the values of features. Then, Feature-FL uses feature selection (i.e., a family of feature-based metrics) to evaluate the relevance of each feature with program failures. Finally, Feature-FL associates each feature with its corresponding statement, and uses the relevance as the suspiciousness to locate suspicious statements. We present six feature-based metrics for Feature-FL , and conduct an extensive study to evaluate the effectiveness of Feature-FL and its potential over the state-of-the-art spectrum-based formulas. Our results provide insight into the potential among different feature-based metrics and also show Feature-FL significantly outperforms the state-of-the-art spectrum-based formulas, e.g., an average saving of at least 30% over spectrum-based formulas in case of real faults.