Deep learning libraries have emerged as critical software systems for a diverse range of deep learning applications. However, the stability and reliability of these libraries are increasingly challenged by their rapidly expanding scale. In this article, we utilize the function call graph as a model to represent deep learning libraries and conduct an empirical study using an innovative approach based on complex network theory. This method facilitates a thorough exploration of the topological characteristics and functionalities of deep learning libraries, revealing their scale-free and small-world properties. Leveraging the characteristics, we utilizek-core decomposition to pinpoint critical functions within the libraries, and conduct a comprehensive analysis to discern the characteristics of their functionalities. Furthermore, we have compiled a comprehensive dataset comprising 12 774 defective functions within these libraries. This dataset enables us to analyze and compare the distribution and trend of defects across the investigated deep learning libraries, while examining the patterns of defect propagation. Our research presents 14 significant findings, offering insights for researchers in software reliability and testing.

Defect prediction is crucial for software quality assurance and has been extensively researched over recent decades. However, prior studies rarely focus on data complexity in defect prediction tasks, and even less on understanding the difficulties of these tasks from the perspective of data complexity. In this article, we conduct an empirical study to estimate the hardness of over 33,000 instances, employing a set of measures to characterize the inherent difficulty of instances and the characteristics of defect datasets. Our findings indicate that: (1) instance hardness in both classes displays a right-skewed distribution, with the defective class exhibiting a more scattered distribution; (2) class overlap is the primary factor influencing instance hardness and can be characterized through feature, structural, and instance-level overlap; (3) no universal preprocessing technique is applicable to all datasets, and it may not consistently reduce data complexity, fortunately, dataset complexity measures can help identify suitable techniques for specific datasets; (4) integrating data complexity information into the learning process can enhance an algorithm’s learning capacity. In summary, this empirical study highlights the crucial role of data complexity in defect prediction tasks, and provides a novel perspective for advancing research in defect prediction techniques.

Software defect prediction (SDP) techniques play a crucial role in identifying defective code regions and improving testing efficiency. Over recent decades, a plethora of SDP approaches has emerged, with machine learning (ML) models being the most widely employed. Despite their superior predictive performance, their black-box nature and uncertainties make it challenging for developers to trust their predictions. To address this issue, we propose a novel trustworthiness score, the adjusted trust score (ATS), which helps determine when to rely on classifier predictions. Furthermore, we employ ATS to develop a reject option for SDP models. Comprehensive experiments on 32 benchmark datasets and six prevalent ML classifiers reveal that high (low) ATS values successfully yield high precision in identifying correct (or incorrect) predictions. ATS also demonstrates superiority over its counterparts, as evidenced by the Wilcoxon signed-rank test. Furthermore, a comparative analysis of prediction performance, with and without a reject option, confirms the feasibility of designing a reject option for SDP models utilizing ATS. Our work highlights that ATS can assist developers in better comprehending the strengths and weaknesses of SDP models. Therefore, it is an essential component for guaranteeing trust from developers and deserves further investigation.

Software aging, which is caused by Aging-Related Bugs (ARBs), tends to occur in long-running systems and may lead to performance degradation and increasing failure rate during software execution. ARB prediction can help developers discover and remove ARBs, thus alleviating the impact of software aging. However, ARB-prone files occupy a small percentage of all the analyzed files. It is usually difficult to gather sufficient ARB data within a project. To overcome the limited availability of training data, several researchers have recently developed cross-project models for ARB prediction. A key point for cross-project models is to learn a good representation for instances in different projects. Nevertheless, most of the previous approaches neither consider the reconstruction property of new representation nor encode source samples' label information in learning representation. To address these shortcomings, we propose a Supervised Representation Learning Approach (SRLA), which is based on double encoding-layer autoencoder, to perform cross-project ARB prediction. Moreover, we present a transfer cross-validation framework to select the hyper-parameters of cross-project models. Experiments on three large open-source projects demonstrate the effectiveness and superiority of our approach compared with the state-of-the-art approach TLAP.

The Android operating system (OS) is a sophisticated man-made system and is the dominant OS in the current smartphone market. Due to the accumulation of errors in the system internal state and the incremental consumption of resources, such as the Dalvik heap memory of software applications and the physical memory, software aging is observed frequently and recognized as a chronic problem of Android smartphones. To mitigate this problem, we propose a two-level software rejuvenation, with the two levels referring to software applications and the OS, in this paper. Based on this strategy, a Markov regenerative process model is constructed to evaluate the steady-state availability and to optimize the time required to trigger rejuvenation for Android smartphones. The parameters of the model, such as the degradation rate and failure rate of software applications and the Android OS, are obtained via our testing platform. Experiments on two real Android applications show that the availability of an Android smartphone increases by 10.81% and 10.18% for the two subjects in our experiments, respectively. An empirical study comparing our two-level strategy with one-level strategies (single application-level and system-level rejuvenation) further verifies the effectiveness of our approach.

In long running systems, software tends to encounter performance degradation and increasing failure rate during execution. This phenomenon has been named software aging, which is caused by aging-related bugs (ARBs). Testing resource allocation can be optimized by identifying ARB-prone modules with ARB prediction. However, due to the low presence and reproducing difficulty of ARBs, it is usually hard to collect sufficient training data to carry out within-project ARB prediction. In this paper, we propose an approach named transfer learning based aging-related bug prediction (TLAP) to perform cross-project ARB prediction. TLAP first takes advantage of transfer learning to reduce distribution difference between training and testing project. Then, class imbalance learning is conducted to mitigate the severe class imbalance between ARB-prone and ARB-free modules. Finally, machine learning methods are used to handle bug prediction tasks. The effectiveness of this approach is validated and evaluated by nine groups of experiments on real software systems. Major conclusions from the experiments include the following: first, TLAP improves cross-project ARB prediction on average compared with traditional machine learning methods; second, utilizing information from multiple-projects can further improve the prediction performance on average. In the best case, it outperforms within-project prediction; third, the number of ARB-prone files and distribution similarity can influence TLAP performance.

The growing popularity and complexity of Android operating system makes it prone to suffer failures during usage, which increases difficulties of fixing bugs. Different strategies and mitigation methods can be developed and applied based on different types of bugs, which gives rise to the necessity to have a deep understanding of the nature of bugs in this system. In this paper, an empirical study is taken on 513 bug reports from Android operating system. A bug classification is conducted according to fault triggering conditions, followed by the analysis of bug types and bug attributes. Moreover, the comparison of bug types between Android and Linux is carried out. This paper reveals ten interesting findings based on the empirical results from these three aspects and further provides guidance for developers and users based on these findings.

In a long running system, software tends to encounter performance degradation and increasing failure rate during execution, which is called software aging. The bugs contributing to the phenomenon of software aging are defined as Aging Related Bugs (ARBs). Lots of manpower and economic costs will be saved if ARBs can be found in the testing phase. However, due to the low presence probability and reproducing difficulty of ARBs, it is usually hard to predict ARBs within a project. In this paper, we study whether and how ARBs can be located through cross-project prediction. We propose a transfer learning based aging related bug prediction approach (TLAP), which takes advantage of transfer learning to reduce the distribution difference between training sets and testing sets while preserving their data variance. Furthermore, in order to mitigate the severe class imbalance, class imbalance learning is conducted on the transferred latent space. Finally, we employ machine learning methods to handle the bug prediction tasks. The effectiveness of our approach is validated and evaluated by experiments on two real software systems. It indicates that after the processing of TLAP, the performance of ARB bug prediction can be dramatically improved.