In software debugging, fault localization (FL) is an essential stage that is used to identify accurate location of faulty statement. It is well known that coverage data plays an important role in FL. In past studies, traditional principal component analysis (PCA) or revised PCA techniques were used to reduce coverage data. However, two kinds of PCA have a great opportunity to remove the actual faulty statement, especially in multiple fault localization. On the other hand, they cannot reflect the status of the statements. In this paper, we propose a novel approach based on revised PCA and incorporate the result of failed and passed test case different combinations to update contribution value of each statement. We used two Linux open-source codes (Sed, Grep) with 4 fault injections to verify the correctness. Preliminary experiments have shown that our proposed method is feasible, scalable, and shorter execution time of FL process, and also can alleviate the situations for removed faulty statements compared to the revised PCA method.

Software defect prediction (SDP) is an essential technique for identifying potential defects in software projects. Generally, SDP is mainly divided into two procedures: extracting features from source code and building a classification model using machine learning methods. However, SDP contends with specific limitations. For example, machine learning models require a fixed input size, but the size of each program is mostly inconsistent. Another limitation is that the amount of training data may be too small for machine learning, and it is extremely difficult to handle class imbalance and dataset expansion. In this study, we propose a method called spatial pyramid pooling for defect prediction (SPP-DP) that first converts all the source files into images, each of which will generate multiple images with different lengths and widths, to address the limitations of class imbalance handling and data augmentation. Second, we input these images into a convolutional neural network (CNN) to build a classifier to predict software defects. We added a spatial pyramid pooling layer (SPP-Layer) architecture to the CNN to relax the limitation of the fixed input size. Compared with different deep learning-based techniques on five datasets, the experimental results show that our proposed SPP-DP is effective, as it can balance the dataset and provide better software defect identification ability.

Research on software reliability growth models (SRGMs) has been extensively conducted for decades, and the models were often developed based on two assumptions: (1) once the errors are detected, they can be completely removed instantly, and (2) errors can be removed eternally, and the debugging tasks will not produce any new errors. However, both assumptions are unrealistic. This study proposes an SRGM that ignores these restricted assumptions by introducing a detection process that may remove an error after a period of time once it has been detected and by considering imperfect debugging, which indicates that new errors may emerge through corresponding debugging tasks. In addition, because software can be upgraded to respond on a timely basis to constantly changing consumer expectations and thus extend product life in the market, the proposed SRGM also considers software upgrades for the multiversion software, and a dynamic programming approach is used to effectively obtain the optimal release schedule with consideration of the constraint of budget. Real data sets are used to examine the effectiveness of the proposed model, and the fitting results show that the proposed model outperforms other existing models. The results of numerical validation indicate that the proposed dynamic programming method with information updating outperforms the sequential solution method in determining the optimal release time for each version. Moreover, decision makers should carefully evaluate the parameters because overestimating the parameters of the mean value functions will cause serious software risk due to excessively shortening the testing time. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0141 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0141 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Abstract As most software reliability models do not clearly explain the variance in the mean value function of cumulative software errors, they might not be effective in deducing the confidence interval regarding the mean value function. In such cases, software developers cannot estimate the possible risk variation in software reliability by using the randomness of the mean value function, thus reducing the decision‐making reliability when determining an optimal software release time. In this paper, the method of stochastic differential equations is used to build a software reliability model, which is validated based on practical data previously used in six published papers. Moreover, the estimation of the parameters of the proposed model, which can be defined as the autonomous error‐detected factor and the learning factor, is also illustrated, and the results of model validation empirically confirm that the proposed model is able to account for a fairly large portion of the variance of the mean value function. Additionally, the confidence intervals of the mean value function regarding software faults are employed to assist software developers in determining the optimal release times at different confidence levels. Finally, a numerical example is given to verify the effectiveness of the proposed model. Copyright © 2008 John Wiley & Sons, Ltd.