Garbage Collection (GC) in the Java Virtual Machine (JVM) serves as an automatic memory management mechanism, efficiently reclaiming unused memory space in different production scenarios. To optimize JVM performance, developers typically fine-tune the garbage collector by identifying an optimal set of GC configurations for specific scenarios. Despite the sophisticated design of garbage collectors, they still have the potential for bugs in different settings, and these bugs can result in more severe consequences. Hence, comprehensive testing of these garbage collectors is imperative before their release. Code coverage criteria are typically employed to assess the comprehensiveness of a test suite. However, traditional code coverage metrics, such as branch coverage, are hardly applicable for GC testing due to their inherent concurrency. Additionally, existing JVM testing techniques do not adequately consider the characteristics of garbage collectors, making it difficult to test these garbage collectors sufficiently. In this article, we make the first effort to design coverage criteria against garbage collectors based on the events of GC called Event-Coverage . Its key insight is to measure the diversity of GC executions for testing purposes by assessing the range of GC events these executions cover. Furthermore, we design a new testing method for maximizing Event-Coverage called GCFuzz . GCFuzz conducts an exhaustive investigation of the memory state space of GC and thoroughly explores the memory state under various GC configurations. To enhance GCFuzz’s efficiency in achieving higher Event-Coverage, we have further designed a coverage-driven strategy for preserving candidate seed programs and selecting GC configurations. Extensive evaluations demonstrate a positive correlation between Event-Coverage and the bug-revealing efficiency. Moreover, GCFuzz outperforms state-of-the-art techniques in detecting unique GC-related inconsistencies and achieving higher Event-Coverage. Remarkably, GCFuzz has identified 20 previously undetected GC bugs, with 15 of them already confirmed or fixed by developers.

Deep Learning (DL) systems have been widely adopted across various industrial domains such as autonomous driving and intelligent healthcare. As with traditional software, DL systems also need to constantly evolve to meet ever-changing user requirements. However, ensuring the quality of these continuously evolving systems presents significant challenges, especially in the context of testing. Understanding how industry developers address these challenges and what extra obstacles they are facing could provide valuable insights for further safeguarding the quality of DL systems. To reach this goal, we conducted semi-structured interviews with 22 DL developers from diverse domains and backgrounds. More specifically, our study focuses on exploring the challenges developers encounter in testing evolving DL systems, the practical solutions they employ, and their expectations for extra support. Our results highlight the difficulties in testing evolving DL systems (e.g., regression faults, online-offline differences, and test data collection) and identify the best practices for D L developers to address these challenges. Additionally, we pinpoint potential future research directions to enhance testing effectiveness in evolving DL systems.

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To evaluate the performance of FeaProtect, we conducted an extensive study comparing it with state-of-the-art approaches. The results show that FeaProtect outperforms the in-processing baseline approaches, with an average improvement of 50.6%–56.4% in terms of regression fault mitigation. We also show that FeaProtect can further enhance the effectiveness of mitigating regression faults by integrating with state-of-the-art post-processing approaches.

Application Programming Interface (API) migration is a common task for adapting software across different programming languages and platforms, where manually constructing the mapping relations between APIs is indeed time-consuming and error-prone. To facilitate this process, many automated API mapping approaches have been proposed. However, existing approaches were mainly designed and evaluated for mapping APIs of statically-typed languages, while their performance on dynamically-typed languages remains unexplored. In this article, we conduct the first extensive study to explore existing API mapping approaches’ performance for mapping APIs in dynamically-typed languages, for which we have manually constructed a high-quality dataset. According to the empirical results, we have summarized several insights. In particular, the source code implementations of APIs can significantly improve the effectiveness of API mapping. However, due to the confidentiality policy, they may not be available in practice. To overcome this, we propose a novel API mapping approach, namedMatl, which leverages the transfer learning technique to learn the semantic embeddings of source code implementations from large-scale open-source repositories and then transfers the learned model to facilitate the mapping of APIs. In this way,Matlcan produce more accurate API embedding of its functionality for more effective mapping without knowing the source code of the APIs. To evaluate the performance ofMatl, we have conducted an extensive study by comparingMatlwith state-of-the-art approaches. The results demonstrate thatMatlis indeed effective as it improves the state-of-the-art approach by at least 18.36% for mapping APIs of dynamically-typed language and by 30.77% for mapping APIs of the statically-typed language.

Deep learning (DL) techniques have attracted much attention in recent years and have been applied to many application scenarios. To improve the performance of DL models regarding different properties, many approaches have been proposed in the past decades, such as improving the robustness and fairness of DL models to meet the requirements for practical use. Among existing approaches, post-training is an effective method that has been widely adopted in practice due to its high efficiency and good performance. Nevertheless, its performance is still limited due to the incompleteness of training data. Additionally, existing approaches are always specifically designed for certain tasks, such as improving model robustness, which cannot be used for other purposes. In this article, we aim to fill this gap and propose an effective and general post-training framework, which can be adapted to improve the model performance from different aspects. Specifically, it incorporates a novel model transformation technique that transforms a classification model into an isomorphic regression model for fine-tuning, which can effectively overcome the problem of incomplete training data by forcing the model to strengthen the memory of crucial input features and thus improve the model performance eventually. To evaluate the performance of our framework, we have adapted it to two emerging tasks for improving DL models, i.e., robustness and fairness improvement, and conducted extensive studies by comparing it with state-of-the-art approaches. The experimental results demonstrate that our framework is indeed general, as it is effective in both tasks. Specifically, in the task of robustness improvement, our approach Dare has achieved the best results on 61.1% cases (vs. 11.1% cases achieved by baselines). In the task of fairness improvement, our approach FMT can effectively improve the fairness without sacrificing the accuracy of the models.

Deep learning (DL) Systems have been widely used in various domains. Similar to traditional software, DL system evolution may also incur regression faults. To find the regression faults between versions of a DL system, we propose a novel regression fuzzing technique called DRFuzz, which facilitates generating inputs that trigger diverse regression faults and have high fidelity. To enhance the diversity of the found regression faults, DRFuzz proposes a diversity-oriented test criterion to explore as many faulty behaviors as possible. Then, DRFuzz incorporates the GAN model to guarantee the fidelity of generated test inputs. We conduct an extensive study on four subjects in four regression scenarios of DL systems. The experimental results demonstrate the superiority of DRFuzz over the two compared state-of-the-art approaches, with an average improvement of 1,177% and 539% in terms of the number of detected regression faults.

Test case prioritization (TCP) aims to schedule the execution order of test cases for earlier fault detection. A recent study has demonstrated that the information-retrieval-based (IR-based) TCP approaches achieve the state-of-the-art effectiveness. The current IR-based TCP approaches leverage lexical similarity between test cases and code changes to guide TCP while ignoring rich code semantics, which may limit their effectiveness to some degree. In this paper, we conduct the first study to explore whether code semantic information can further boost IR-based TCP. Here, we studied two types of code representation methods (i.e., general-purpose and task-associated models) and explored two modes of utilizing the code representation embeddings (i.e., unsupervised and supervised modes) for IR-based TCP. Our results demonstrate that incorporating code semantics through the supervised mode of code representation can achieve a 16.96% improvement in the efficiency of fault detection over the state-of-the-art IR-based TCP approach (which is based on lexical similarity).

Deep learning (DL) techniques have attracted much attention in recent years, and have been applied to many application scenarios, including those that are safety-critical. Improving the universal robustness of DL models is vital and many approaches have been proposed in the last decades aiming at such a purpose. Among existing approaches, adversarial training is the most representative. It advocates a post model tuning process via incorporating adversarial samples. Although successful, they still suffer from the challenge of generalizability issues in the face of various attacks with unsatisfactory effectiveness. Targeting this problem, in this paper we propose a novel model training framework, which aims at improving the universal robustness of DL models via model transformation incorporated with a data augmentation strategy in a delta debugging fashion. We have implemented our approach in a tool, called Dare, and conducted an extensive evaluation on 9 DL models. The results show that our approach significantly outperforms existing adversarial training techniques. Specifically, Dare has achieved the highest Empirical Robustness in 29 of 45 testing scenarios under various attacks, while the number drops to 5 of 45 for the best baseline approach.

For programmers, learning the usage of APIs (Application Programming Interfaces) of a software library is important yet difficult.API recommendation tools can help developers use APIs by recommending which APIs to be used next given the APIs that have been written.Traditionally, language models such as N-gram are applied to API recommendation.However, because the software libraries keep changing and new libraries keep emerging, new APIs are common.These new APIs can be seen as OOV (out of vocabulary) words and cannot be handled well by existing API recommendation approaches due to the lack of training data.In this paper, we propose APIRecX, the first cross-library API recommendation approach, which uses BPE to split each API call in each API sequence and pre-trains a GPTbased language model.It then recommends APIs by fine-tuning the pre-trained model.APIRecX can migrate the knowledge of existing libraries to a new library, and can recommend APIs that are previously regarded as OOV.We evaluate APIRecX on six libraries and the results confirm its effectiveness by comparing with two typical API recommendation approaches.

Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is very costly, which could largely affect the efficiency of DNN testing, even the whole process of DNN development. To relieve the labeling-cost problem, we propose a novel test input prioritization approach (called PRIMA) for DNNs via intelligent mutation analysis in order to label more bug-revealing test inputs earlier for a limited time, which facilitates to improve the efficiency of DNN testing. PRIMA is based on the key insight: a test input that is able to kill many mutated models and produce different prediction results with many mutated inputs, is more likely to reveal DNN bugs, and thus it should be prioritized higher. After obtaining a number of mutation results from a series of our designed model and input mutation rules for each test input, PRIMA further incorporates learning-to-rank (a kind of supervised machine learning to solve ranking problems) to intelligently combine these mutation results for effective test input prioritization. We conducted an extensive study based on 36 popular subjects by carefully considering their diversity from five dimensions (i.e., different domains of test inputs, different DNN tasks, different network structures, different types of test inputs, and different training scenarios). Our experimental results demonstrate the effectiveness of PRIMA, significantly outperforming the state-of-the-art approaches (with the average improvement of 8.50%~131.01% in terms of prioritization effectiveness). In particular, we have applied PRIMA to the practical autonomous-vehicle testing in a large motor company, and the results on 4 real-world scene-recognition models in autonomous vehicles further confirm the practicability of PRIMA.

Deep neural network (DNN) has become increasingly popular and DNN testing is very critical to guarantee the correctness of DNN, i.e., the accuracy of DNN in this work. However, DNN testing suffers from a serious efficiency problem, i.e., it is costly to label each test input to know the DNN accuracy for the testing set, since labeling each test input involves multiple persons (even with domain-specific knowledge) in a manual way and the testing set is large-scale. To relieve this problem, we propose a novel and practical approach, called PACE (which is short for P ractical AC curacy E stimation), which selects a small set of test inputs that can precisely estimate the accuracy of the whole testing set. In this way, the labeling costs can be largely reduced by just labeling this small set of selected test inputs. Besides achieving a precise accuracy estimation, to make PACE more practical it is also required that it is interpretable, deterministic, and as efficient as possible. Therefore, PACE first incorporates clustering to interpretably divide test inputs with different testing capabilities (i.e., testing different functionalities of a DNN model) into different groups. Then, PACE utilizes the MMD-critic algorithm, a state-of-the-art example-based explanation algorithm, to select prototypes (i.e., the most representative test inputs) from each group, according to the group sizes, which can reduce the impact of noise due to clustering. Meanwhile, PACE also borrows the idea of adaptive random testing to select test inputs from the minority space (i.e., the test inputs that are not clustered into any group) to achieve great diversity under the required number of test inputs. The two parallel selection processes (i.e., selection from both groups and the minority space) compose the final small set of selected test inputs. We conducted an extensive study to evaluate the performance of PACE based on a comprehensive benchmark (i.e., 24 pairs of DNN models and testing sets) by considering different types of models (i.e., classification and regression models, high-accuracy and low-accuracy models, and CNN and RNN models) and different types of test inputs (i.e., original, mutated, and automatically generated test inputs). The results demonstrate that PACE is able to precisely estimate the accuracy of the whole testing set with only 1.181%∼2.302% deviations, on average, significantly outperforming the state-of-the-art approaches.