Recently LLMs have faced increasing demands to selectively remove specific information through Machine Unlearning. While evaluating unlearning effectiveness is crucial, existing benchmarks suffer from fundamental limitations in audit dataset generation from unstructured corpora. We identify two critical challenges: ensuring audit adequacy and handling knowledge redundancy between forget and retain datasets. Current approaches rely on ad-hoc question generation from unstructured text, leading to unpredictable coverage gaps and evaluation blind spots. Knowledge redundancy between forget and retain corpora further obscures evaluation, making it difficult to distinguish genuine unlearning failures from legitimately retained knowledge. To bring clarity to this challenge, we propose LUCID, an automated framework that leverages knowledge graphs to achieve comprehensive audit dataset generation with fine-grained coverage and systematic redundancy elimination. By converting unstructured corpora into structured knowledge representations, it transforms the ad-hoc audit dataset generation process into a transparent and automated generation pipeline that ensures both adequacy and non-redundancy. Applying LUCID to the MUSE benchmark, we generated over 69,000 and 111,000 audit cases for News and Books datasets respectively, identifying thousands of previously undetected knowledge memorization instances. Our analysis reveals that knowledge redundancy significantly skews metrics, artificially inflating ROUGE from 19.7% to 26.1% and Entailment Scores from 32.4% to 35.2%, highlighting the necessity of deduplication for accurate assessment.

Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs) remain largely unexplored.In this paper, we identify emoticon semantic confusion, a vulnerability where LLMs misinterpret ASCII-based emoticons to perform unintended and even destructive actions.To systematically study this phenomenon, we develop an automated data generation pipeline and construct a dataset containing 3,757 code-oriented test cases spanning 21 meta-scenarios, four programming languages, and varying contextual complexities.Our study on six LLMs reveals that emoticon semantic confusion is pervasive, with an average confusion ratio exceeding 38%.More critically, over 90% of confused responses yield 'silent failures', which are syntactically valid outputs but deviate from user intent, potentially leading to destructive security consequences.Furthermore, we observe that this vulnerability readily transfers to popular agent frameworks, while existing prompt-based mitigations remain largely ineffective.We call on the community to recognize this emerging vulnerability and develop effective mitigation methods to uphold the safety and reliability of human-LLM interactions.* These authors contributed equally.

Solidity is the primary programming language for writing smart contracts. As a lightweight language, Solidity does not have a unified way to manage third-party library (TPL) dependencies. Instead, the copy-and-paste pattern and other dependency managers such as NPM and Git submodules have become alternatives. However, these mechanisms significantly increase the complexity of TPL usage with security concerns. Similar to other programming language ecosystems, incorrect TPL usage can influence the reliability of contracts and even introduce vulnerabilities from outdated versions. Therefore, there is an urgent need to understand and comprehend Solidity TPL dependencies. In this work, we conduct a comprehensive study on TPL dependency usage in Solidity. To achieve this, we first present SPADE , which leverages additional metadata (e.g., package and remapping configurations) to infer fine-grained TPL dependencies with version and contract details in various Solidity projects. With SPADE , we investigate a broad spectrum of 5,242 Solidity repositories to understand the TPL dependencies, including their landscape and version-level usage. Our research reveals a set of interesting and important findings that can be beneficial to the Solidity ecosystem. In particular, TPL dependencies are prevalent in Solidity, but the version management remains inadequate. The propagation of vulnerability is severe, affecting 8.87% of the repositories. Finally, we use on-chain contracts to validate the findings and provide suggestions for future research and development on Solidity TPL dependencies.

Deep Learning (DL) frameworks are fundamental components of DL systems in their development, deployment, and execution, while defects in DL frameworks can cause severe consequences. Ensuring the quality of DL frameworks has therefore become a pressing challenge. Among the various testing techniques, model mutation has emerged as a widely adopted approach. Such methods generate mutants by applying mutation operators to DL models (e.g., structural changes or parameter edits) and then analyzing inconsistencies, crashes, or abnormal behaviors across different frameworks or hardware. Despite its effectiveness, existing methods suffer from the following limitations. First, they mainly reuse operators designed for model testing, raising doubts about their ability to expose framework-level defects. Besides, they insufficiently consider mutation constraints, such as mutation type, position, and order, which directly affect the defect detection ability of generated mutants. Finally, they rely on the limited detection range and narrow test oracles, focusing on functional correctness in model inference while overlooking defects in efficiency, resource usage, and other defects that developers care about in other stages, such as model training or deployment. These limitations result in a weak alignment with the critical defects that developers are most concerned about in practice. Motivated by these observations, this study conducts a comprehensive investigation into the effectiveness of existing mutation-based testing methods. We first collect and classify defect reports from PyTorch and MindSpore according to developers’ priority tags, building a taxonomy of seven categories and 19 sub-categories of HP defects. We then map the defects reported by five state-of-the-art methods into this taxonomy to evaluate their detection abilities. To explain these limitations, we further analyze how three key factors, mutation type, mutation position, and mutation order, affect the generated mutants. Based on the experiment results, we summarize ten findings ranging from revealing the priority of developers on fixing framework defects, evaluating the defect detection ability of existing methods, to how mutation factors affect the generated mutants. Furthermore, we reveal four limitations and their root causes of existing methods and propose four targeted optimization strategies. We further apply these strategies to COMET and successfully uncover six new defects spanning four types, including two previously unreported categories. Overall, our study identifies 38 unique framework defects, of which 30 are confirmed by developers and 12 have been fixed, demonstrating the practical value of our findings.

Xiaoyu Zhang, Juan Zhai, Shiqing Ma, Qingshuang Bao, Weipeng Jiang, Qian Wang, Chao Shen, Yang Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Task-specific fine-tuning is essential for the deployment of large language models (LLMs), but it requires significant computational resources and time. Existing solutions have proposed coreset selection methods to improve data efficiency and reduce model training overhead, but they still have limitations: ❶ Overlooking valuable samples at high pruning rates, which degrades the coreset’s performance. ❷ Requiring high time overhead during coreset selection to fine-tune and evaluate the target LLM. In this paper, we introduce STAFF, a speculative coreset selection method. STAFF leverages a small model from the same family as the target LLM to efficiently estimate data scores and then verifies the scores on the target LLM to accurately identify and allocate more selection budget to important regions while maintaining coverage of easy regions. We evaluate STAFF on three LLMs and three downstream tasks and show that STAFF improves the performance of SOTA methods by up to 54.3% and reduces selection overhead by up to 70.5% at different pruning rates. Furthermore, we observe that the coreset selected by STAFF at low pruning rates (i.e., 20%) can even obtain better fine-tuning performance than the full dataset.

With the widespread adoption of machine learning models, especially deep neural networks (DNNs), as an integral part of new intelligent software, the new tools to effectively support the model engineering and debugging process have received extensive attention. However, the existing tools only provide limited support for the training process. They are either post-training tools that fail to detect problems timely, resulting in wasting time and resources on training buggy models, or merely collecting the training data and still require manual analysis. In this paper, we proposeAutoTrainer, an automated monitoring and repairing system for DNN training, which provides real-time monitoring for the model training process and automatically repairs eight commonly seen training problems.AutoTrainermonitors the training process and detects potential training problems. For any detected problem,AutoTrainertries to fix it with the built-in state-of-the-art solutions. Our experiments on six datasets and 701 models show that the problem detection accuracy ofAutoTrainerreaches 100% without false positives. Moreover, it fixes 98.42% of all detected problems and improves the model accuracy by 36.42% on average.

Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and hyperparameters for a given task. Like other software systems, existing AutoML systems have shortcomings in their design. We identify two common and severe shortcomings in AutoML, performance issue (i.e., searching for the desired model takes an unreasonably long time) and ineffective search issue (i.e., AutoML systems are not able to find an accurate enough model). After analyzing the workflow of AutoML, we observe that existing AutoML systems overlook potential opportunities in search space, search method, and search feedback, which results in performance and ineffective search issues. Based on our analysis, we design and implement DREAM , an automatic and general-purpose tool to alleviate and repair the shortcomings of AutoML pipelines and conduct effective model searches for diverse tasks. It monitors the process of AutoML to collect detailed feedback and automatically repairs shortcomings by expanding search space and leveraging a feedback-driven search strategy. Our evaluation results show that DREAM can be applied on two state-of-the-art AutoML pipelines and effectively and efficiently repair their shortcomings.

eBPF (extended Berkeley Packet Filter) significantly enhances observability, performance, and security within the Linux kernel, playing a pivotal role in various real-world applications. Implemented as a register-based kernel virtual machine, eBPF features a customized Instruction Set Architecture (ISA) with stringent kernel safety requirements, e.g., a limited number of instructions. This constraint necessitates substantial optimization efforts for eBPF programs to meet performance objectives. Despite the availability of compilers supporting eBPF program compilation, existing tools often overlook key optimization opportunities, resulting in suboptimal performance. In response, this paper introduces Merlin, an optimization framework leveraging customized LLVM passes and bytecode rewriting for Instruction Representation (IR) transformation and bytecode refinement. Merlin employs two primary optimization strategies, i.e., instruction merging and strength reduction. These optimizations are deployed before eBPF verification. We evaluate Merlin across 19 XDP programs (drawn from the Linux kernel, Meta, hXDP, and Cilium) and three eBPF-based systems (Sysdig, Tetragon, and Tracee, each comprising several hundred eBPF programs). The results show that all optimized programs pass the kernel verification. Meanwhile, Merlin can reduce number of instructions by 73% and runtime overhead by 60% compared with the original programs. Merlin can also improve the throughput by 0.59% and reduce the latency by 5.31%, compared to state-of-the-art technique K2, while being 106 times faster and more scalable to larger and more complex programs without additional manual efforts.

The robustness of convolutional neural networks (CNNs) is vital to modern AI-driven systems. It can be quanti-fied by formal verification by providing a certified lower bound, within which any perturbation does not alter the original input's classification result. It is challenging due to nonlinear components, such as MaxPool. At present, many verification methods are sound but risk losing some precision to enhance efficiency and scalability, and thus, a certified lower bound is a crucial criterion for evaluating the performance of verification tools. In this paper, we present MaxLin, a robustness verifier for MaxPool-based CNNs with tight Linear approximation. By tight-ening the linear approximation of the MaxPool function, we can certify larger certified lower bounds of CNNs. We evaluate MaxLin with open-sourced benchmarks, including LeNet and networks trained on the MNIST, CIFAR-10, and Tiny ImageNet datasets. The results show that MaxLin outperforms state-of-the-art tools with up to 110.60% improvement regarding the certified lower bound and 5.13 × speedup for the same neural networks. Our code is available at https://github.com/xiaoyuanpigo/maxlin.

With the emergence of the Software 3.0 era, there is a growing trend of compressing and integrating large models into software systems, with significant societal implications. Regrettably, in numerous instances, model compression techniques impact the fairness performance of these models and thus the ethical behavior of DNN-powered software. One of the most notable example is the Lottery Ticket Hypothesis (LTH), a prevailing model pruning approach. This paper demonstrates that fairness issue of LTH-based pruning arises from both its subnetwork selection and training procedures, highlighting the inadequacy of existing remedies. To address this, we propose a novel pruning framework, Ballot, which employs a novel conflict-detection-based subnetwork selection to find accurate and fair subnetworks, coupled with a refined training process to attain a high-performance model, thereby improving the fairness of DNN-powered software. By means of this procedure, Ballot improves the fairness of pruning by 38.00%, 33.91%, 17.96%, and 35.82% compared to state-of-the-art baselines, namely Magnitude Pruning, Standard LTH, SafeCompress, and FairScratch respectively, based on our evaluation of five popular datasets and three widely used models. Our code is available at https://anonymous.4open.science/r/Ballot-506E.

Deep learning (DL) frameworks are the fundamental infrastructure for various DL applications. Framework defects can profoundly cause disastrous accidents, thus requiring sufficient detection. In previous studies, researchers adopt DL models as test inputs combined with mutation to generate more diverse models. Though these studies demonstrate promising results, most detected defects are considered trivial (i.e., either treated as edge cases or ignored by the developers). To identify important bugs that matter to developers, we propose a novel DL framework testing method DevMuT, which generates models by adopting mutation operators and constraints derived from developer expertise. DevMuT simulates developers' common operations in development and detects more diverse defects within more stages of the DL model lifecycle (e.g., model training and inference). We evaluate the performance of DevMuT on three widely used DL frameworks (i.e., PyTorch, JAX, and Mind-Spore) with 29 DL models from nine types of industry tasks. The experiment results show that DevMuT outperforms state-of-the-art baselines: it can achieve at least 71.68% improvement on average in the diversity of generated models and 28.20% improvement on average in the legal rates of generated models. Moreover, DevMuT detects 117 defects, 63 of which are confirmed, 24 are fixed, and eight are of high value confirmed by developers. Finally, DevMuT has been deployed in the MindSpore community since December 2023. These demonstrate the effectiveness of DevMuT in detecting defects that are close to the real scenes and are of concern to developers.

In recent years, Deep Learning (DL) applications in JavaScript environment have become increasingly popular. As the infrastructure for DL applications, JavaScript DL frameworks play a crucial role in the development and deployment. It is essential to ensure the quality of JavaScript DL frameworks. However, the bottleneck of limited computational resources in the JavaScript environment brings new challenges to framework testing. Specifically, JavaScript DL frameworks are equipped with various optimization mechanisms (e.g., cache reuse, inference acceleration) to overcome the bottleneck of limited computational resources. These optimization mechanisms are overlooked by existing methods, resulting in many bugs in JavaScript DL frameworks being missed. To address the above challenges, we propose a mutation-based JavaScript DL framework testing method named DLJSFuzzer. DLJSFuzzer designs 13 tensor mutation rules targeting the cache reuse mechanism to generate test input tensors. Besides, DLJSFuzzer designs eight model mutation rules targeting the inference acceleration mechanism to generate test input models. To evaluate the effectiveness of DLJS-Fuzzer, we conduct experiments on the most widely-used JavaScript DL framework, TensorFlow.js. The experimental results show that DLJSFuzzer outperforms state-of-the-art methods in both effectiveness and efficiency. DLJSFuzzer successfully detects 21 unique crashes and 126 unique NaN & Inconsistency bugs. All detected crashes have been reported to the open-source community, with 12 of them already confirmed by developers. Additionally, DLJSFuzzer has improved by over 47% in model generation efficiency and over 91% in bug detection efficiency compared to all baselines.

The rapid advancement of intelligent connected vehicles (ICVs) in the automotive sector has significantly intensified security and privacy issues. Particularly, the previous studies have indicated that the ICV users (owners) are deeply concerned about the extensive data gathered by these vehicles. However, current research into vehicle security predominantly concentrates on the analysis and discussion of sensitive data from ICVs of specific brands or models. There is a notable lack of studies that conduct a comprehensive, large-scale investigation into the sensitive data collected by ICVs and assess the privacy implications of such data collection. In this article, we undertake an extensive investigation to comprehend the privacy risks associated with Internet-connected vehicles (ICVs) through their companion mobile apps. To accomplish this, we have devised a semi-automatic pipeline leveraging program analysis and large language model (LLM) to identify and track sensitive data across these apps. Specifically, we begin by constructing a detailed knowledge base of ICV sensitive data extracted from the privacy policies of companion apps. Subsequently, we conduct static analysis on the car companion apps, pinpointing instances of sensitive data usage within the app code and analysing their potential privacy risks. Our analysis, covering 401 car companion apps spanning 271 unique vehicle brands, unveils several noteworthy findings concerning the usage of user sensitive data in the ICV ecosystem. For instance, various entities within the ICV ecosystem collect a wide array of sensitive data, including brake status, passenger occupancy, and insurance details. Alarmingly, we discover that 37.91% of car companion apps fail to adequately disclose their data usage practices. Moreover, we observe extensive involvement of entities beyond vehicle manufacturers in the handling of vehicle sensitive data, including data analytics companies, charging service providers, and cloud service vendors.

Machine translation systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe consequences, such as financial losses. This requires to improve the accuracy and the reliability of machine translation systems. However, it is challenging to test machine translation systems because of the complexity and intractability of the underlying neural models. To tackle these challenges, we propose a novel metamorphic testing approach by syntactic tree pruning (STP) to validate machine translation systems. Our key insight is that a pruned sentence should have similar crucial semantics compared with the original sentence. Specifically, STP (1) proposes a core semantics-preserving pruning strategy by basic sentence structures and dependency relations on the level of syntactic tree representation, (2) generates source sentence pairs based on the metamorphic relation, and (3) reports suspicious issues whose translations break the consistency property by a bag-of-words model. We further evaluate STP on two state-of-the-art machine translation systems (i.e., Google Translate and Bing Microsoft Translator) with 1,200 source sentences as inputs. The results show that STP accurately finds 5,073 unique erroneous translations in Google Translate and 5,100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64.5% and 65.4% precision, respectively. The reported erroneous translations vary in types and more than 90% of them are not found by state-of-the-art techniques. There are 9,393 erroneous translations unique to STP, which is 711.9% more than state-of-the-art techniques. Moreover, STP is quite effective in detecting translation errors for the original sentences with a recall reaching 74.0%, improving state-of-the-art techniques by 55.1% on average.

Due to the model aging problem, Deep Neural Networks (DNNs) need updates to adjust them to new data distributions. The common practice leverages incremental learning (IL), e.g., Class-based Incremental Learning (CIL) that updates output labels, to update the model with new data and a limited number of old data. This avoids heavyweight training (from scratch) using conventional methods and saves storage space by reducing the number of old data to store. But it also leads to poor performance in fairness. In this paper, we show that CIL suffers both dataset and algorithm bias problems, and existing solutions can only partially solve the problem. We propose a novel framework, CILIATE, that fixes both dataset and algorithm bias in CIL. It features a novel differential analysis guided dataset and training refinement process that identifies unique and important samples overlooked by existing CIL and enforces the model to learn from them. Through this process, CILIATE improves the fairness of CIL by 17.03%, 22.46%, and 31.79% compared to state-of-the-art methods, iCaRL, BiC, and WA, respectively, based on our evaluation on three popular datasets and widely used ResNet models. Our code is available at https://github.com/Antimony5292/CILIATE.

Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns (e.g., a patch or image transformations) as triggers, which are vulnerable to human inspection. In this paper, we propose stealthy and efficient Trojan attacks, BppAttack. Based on existing biology literature on human visual systems, we propose to use image quantization and dithering as the Trojan trigger, making imperceptible changes. It is a stealthy and efficient attack without training auxiliary models. Due to the small changes made to images, it is hard to inject such triggers during training. To alleviate this problem, we propose a contrastive learning based approach that leverages adversarial attacks to generate negative sample pairs so that the learned trigger is precise and accurate. The proposed method achieves high attack success rates on four benchmark datasets, including MNIST, CIFAR-10, GTSRB, and CelebA. It also effectively bypasses existing Trojan defenses and human inspection. Our code can be found in https://github.com/RU-System-Software-and-Security/BppAttack.

With Deep Neural Network (DNN) being integrated into a growing number of critical systems with far-reaching impacts on society, there are increasing concerns on their ethical performance, such as fairness. Unfortunately, model fairness and accuracy in many cases are contradictory goals to optimize during model training. To solve this issue, there has been a number of works trying to improve model fairness by formalizing an adversarial game in the model level. This approach introduces an adversary that evaluates the fairness of a model besides its prediction accuracy on the main task, and performs joint-optimization to achieve a balanced result. In this paper, we noticed that when performing backward propagation based training, such contradictory phenomenon are also observable on individual neuron level. Based on this observation, we propose FairNeuron, a DNN model automatic repairing tool, to mitigate fairness concerns and balance the accuracy-fairness trade-off without introducing another model. It works on detecting neurons with contradictory optimization directions from accuracy and fairness training goals, and achieving a trade-off by selective dropout. Comparing with state-of-the-art methods, our approach is lightweight, scaling to large models and more efficient. Our evaluation on three datasets shows that FairNeuron can effectively improve all models' fairness while maintaining a stable utility.

The backdoor or Trojan attack is a severe threat to deep neural networks (DNNs). Researchers find that DNNs trained on benign data and settings can also learn backdoor behaviors, which is known as the natural backdoor. Existing works on anti-backdoor learning are based on weak observations that the backdoor and benign behaviors can differentiate during training. An adaptive attack with slow poisoning can bypass such defenses. Moreover, these methods cannot defend natural backdoors. We found the fundamental differences between backdoor-related neurons and benign neurons: backdoor-related neurons form a hyperplane as the classification surface across input domains of all affected labels. By further analyzing the training process and model architectures, we found that piece-wise linear functions cause this hyperplane surface. In this paper, we design a novel training method that forces the training to avoid generating such hyperplanes and thus remove the injected backdoors. Our extensive experiments on five datasets against five state-of-the-art attacks and also benign training show that our method can outperform existing state-of-the-art defenses. On average, the ASR (attack success rate) of the models trained with NONE is 54.83 times lower than undefended models under standard poisoning backdoor attack and 1.75 times lower under the natural backdoor attack. Our code is available at https://github.com/RU-System-Software-and-Security/NONE.

Deep Neural Networks are vulnerable to Trojan (or backdoor) attacks. Reverse-engineering methods can reconstruct the trigger and thus identify affected models. Existing reverse-engineering methods only consider input space constraints, e.g., trigger size in the input space.Expressly, they assume the triggers are static patterns in the input space and fail to detect models with feature space triggers such as image style transformations. We observe that both input-space and feature-space Trojans are associated with feature space hyperplanes.Based on this observation, we design a novel reverse-engineering method that exploits the feature space constraint to reverse-engineer Trojan triggers. Results on four datasets and seven different attacks demonstrate that our solution effectively defends both input-space and feature-space Trojans. It outperforms state-of-the-art reverse-engineering methods and other types of defenses in both Trojaned model detection and mitigation tasks. On average, the detection accuracy of our method is 93%. For Trojan mitigation, our method can reduce the ASR (attack success rate) to only 0.26% with the BA (benign accuracy) remaining nearly unchanged. Our code can be found at https://github.com/RU-System-Software-and-Security/FeatureRE.

With machine learning models especially Deep Neural Network (DNN) models becoming an integral part of the new intelligent software, new tools to support their engineering process are in high demand. Existing DNN debugging tools are either post-training which wastes a lot of time training a buggy model and requires expertises, or limited on collecting training logs without analyzing the problem not even fixing them. In this paper, we propose AUTOTRAINER, a DNN training monitoring and automatic repairing tool which supports detecting and auto repairing five commonly seen training problems. During training, it periodically checks the training status and detects potential problems. Once a problem is found, AUTOTRAINER tries to fix it by using built-in state-of-the-art solutions. It supports various model structures and input data types, such as Convolutional Neural Networks (CNNs) for image and Recurrent Neural Networks (RNNs) for texts. Our evaluation on 6 datasets, 495 models show that AUTOTRAINER can effectively detect all potential problems with 100% detection rate and no false positives. Among all models with problems, it can fix 97.33% of them, increasing the accuracy by 47.08% on average.

Ordinary users can build their smart home automation system easily nowadays, but such user-customized systems could be error-prone. Using formal verification to prove the correctness of such systems is necessary. However, to conduct formal proof, formal specifications such as Linear Temporal Logic (LTL) formulas have to be provided, but ordinary users cannot author LTL formulas but only natural language.To address this problem, this paper presents a novel approach that can automatically generate formal LTL specifications from natural language requirements based on domain knowledge and our proposed ambiguity refining techniques. Experimental results show that our approach can achieve a high correctness rate of 95.4% in converting natural language sentences into LTL formulas from 481 requirements of real examples.

Code comments provide abundant information that have been leveraged to help perform various software engineering tasks, such as bug detection, specification inference, and code synthesis. However, developers are less motivated to write and update comments, making it infeasible and error-prone to leverage comments to facilitate software engineering tasks. In this paper, we propose to leverage program analysis to systematically derive, refine, and propagate comments. For example, by propagation via program analysis, comments can be passed on to code entities that are not commented such that code bugs can be detected leveraging the propagated comments. Developers usually comment on different aspects of code elements like methods, and use comments to describe various contents, such as functionalities and properties. To more effectively utilize comments, a fine-grained and elaborated taxonomy of comments and a reliable classifier to automatically categorize a comment are needed. In this paper, we build a comprehensive taxonomy and propose using program analysis to propagate comments. We develop a prototype CPC, and evaluate it on 5 projects. The evaluation results demonstrate 41573 new comments can be derived by propagation from other code locations with 88% accuracy. Among them, we can derive precise functional comments for 87 native methods that have neither existing comments nor source code. Leveraging the propagated comments, we detect 37 new bugs in open source large projects, 30 of which have been confirmed and fixed by developers, and 304 defects in existing comments (by looking at inconsistencies between existing and propagated comments), including 12 incomplete comments and 292 wrong comments. This demonstrates the effectiveness of our approach. Our user study confirms propagated comments align well with existing comments in terms of quality.

Lock is a frequently-used synchronization mechanism to enforce exclusive access to a shared resource. However, lock-based concurrent programs are susceptible to lock contention, which leads to low performance and poor scalability. Furthermore, inappropriate granularity of a lock makes lock contention even worse. Compared to coarse-grained lock, fine-grained lock can mitigate lock contention but difficult to use. Converting coarse-grained lock into fine-grained lock manually is not only error-prone and tedious, but also requires a lot of expertise. In this paper, we propose to leverage program analysis techniques and pushdown automaton to automatically covert coarse-grained locks into fine-grained locks to reduce lock contention. We developed a prototype FineLock and evaluates it on 5 projects. The evaluation results demonstrate FineLock can refactor 1,546 locks in an average of 27.6 seconds, including converting 129 coarse-grained locks into fine-grained locks and 1,417 coarse-grained locks into read/write locks. By automatically providing potential refactoring recommendations, our tool saves a lot of efforts for developers.

Inspired by the great success of using code coverage as guidance in software testing, a lot of neural network coverage criteria have been proposed to guide testing of neural network models (e.g., model accuracy under adversarial attacks). However, while the monotonic relation between code coverage and software quality has been supported by many seminal studies in software engineering, it remains largely unclear whether similar monotonicity exists between neural network model coverage and model quality. This paper sets out to answer this question. Specifically, this paper studies the correlation between DNN model quality and coverage criteria, effects of coverage guided adversarial example generation compared with gradient decent based methods, effectiveness of coverage based retraining compared with existing adversarial training, and the internal relationships among coverage criteria.

Formal program specifications are essential for various software engineering tasks, such as program verification, program synthesis, code debugging and software testing. However, manually inferring formal program specifications is not only time-consuming but also error-prone. In addition, it requires substantial expertise. Natural language comments contain rich semantics about behaviors of code, making it feasible to infer program specifications from comments. Inspired by this, we develop a tool, named C2S, to automate the specification synthesis task by translating natural language comments into formal program specifications. Our approach firstly constructs alignments between natural language word and specification tokens from existing comments and their corresponding specifications. Then for a given method comment, our approach assembles tokens that are associated with words in the comment from the alignments into specifications guided by specification syntax and the context of the target method. Our tool successfully synthesizes 1,145 specifications for 511 methods of 64 classes in 5 different projects, substantially outperforming the state-of-the-art. The generated specifications are also used to improve a number of software engineering tasks like static taint analysis, which demonstrates the high quality of the specifications.

The development of mobile apps has become the current mantra for any business' success. The rise of many types of mobile devices and mobile OS has instantly created the need to develop multiple versions for the same app. In order to grasp as much market share as possible, it is desirable to have all the versions of an app demonstrate similar user interface (UI) appearances, to make users feel comfortable when switching from one platform to another and more likely to stick to the app. However, to ensure consistent UIs among cross-platform versions can be a challenging and costly endeavor, since different platforms have their own UI controls and programming languages. In this paper, we propose an automatic approach to transforming mobile app UIs across platforms, and illustrate our approach by transforming the UIs of iOS apps to Android ones. We leverage the enormous existing apps carefully designed by developers to achieve similar UI effects between iOS and Android versions, since these apps contain valuable knowledge of mapping relations between the iOS and Android UI controls. Starting from the reverse engineering of these apps, our approach separates each user interface into modules of adequate sizes. Then it maps the modules from both versions that contribute to the same visual and functional effect, and automatically mines the mapping relations. By applying the mined relations, our approach has successfully transformed the iOS app UIs into Android app UIs, as confirmed by a series of experiments.

Preferences, the setting options provided by Android, are an essential part of Android apps. Preferences allow users to change app features and behaviors dynamically, and therefore, need to be thoroughly tested. Unfortunately, the specific preferences used in test cases are typically not explicitly specified, forcing testers to manually set options or blindly try different option combinations. To effectively test the impacts of different preference options, this paper presents PREFEST, as a preference-wise enhanced automatic testing approach, for Android apps. Given a set of test cases, PREFEST can locate the preferences that may affect the test cases with a static and dynamic combined analysis on the app under test, and execute these test cases only under necessary option combinations. The evaluation shows that PREFEST can improve 6.8% code coverage and 12.3% branch coverage and find five more real bugs compared to testing with the original test cases. The test cost is reduced by 99% for both the number of test cases and the testing time, compared to testing under pairwise combination of options.

Modern Android malwares tend to use advanced techniques to cover their malicious behaviors. They usually feature multi-staged, condition-guarded and environment-specific payloads. An increasing number of them utilize WebView, particularly the two-way communications between Java and JavaScript, to evade detection and analysis of existing techniques. We propose Dual-Force, a forced execution technique which simultaneously forces both Java and JavaScript code of WebView applications to execute along various paths without requiring any environment setup or providing any inputs manually. As such, the hidden payloads of WebView malwares are forcefully exposed. The technique features a novel execution model that allows forced execution to suppress exceptions and continue execution. Experimental results show that Dual-Force precisely exposes malicious payload in 119 out of 150 WebView malwares. Compared to the state-of-the-art, Dual-Force can expose 23% more malicious behaviors.

Abstract interpretation is capable of inferring a wide variety of quantifier-free program invariants. In this paper, we propose a general framework for building universally quantified abstract domains that leverage existing quantifier-free domains in induction-loop programs. This method is sound and converges in finite time. We instantiate this framework using two quantifier-free domains: difference-bound matrices with disequality constraints (dDBM) domain and polynomial equations domain. The experiments on a variety of programs using arrays demonstrate the feasibility of the approach.

Encapsulation is one of the basic characteristics of object-oriented programming. However, the access modifiers provided by common object-oriented languages do not help much because they only encapsulate the member references rather than the objects pointed to by them. Bad encapsulation makes object-oriented programs difficult to understand and reason about, thus concealing potential software vulnerabilities. We present in this paper the encapsulation analysis technique, which is an expression-based dataflow analysis, to statically compute the runtime memory layouts of object-oriented programs. The analysis results can help developers to master an intuitive comprehension on the code quality regarding encapsulation of classes. The results of experiments on various open-source Java projects and libraries show that our approach is both effective (25.76% of the classes are reported as not fully encapsulated) and efficient (2.15 KLOC/s and 27.35 classes/s) in finding potential encapsulation problems. We also give common guidance on how to achieve better encapsulation for object-oriented programs.

Data provenance tracking determines the set of inputs related to a given output. It enables quality control and problem diagnosis in data engineering. Most existing techniques work by tracking program dependencies. They cannot quantitatively assess the importance of related inputs, which is critical to machine learning algorithms, in which an output tends to depend on a huge set of inputs while only some of them are of importance. In this paper, we propose LAMP, a provenance computation system for machine learning algorithms. Inspired by automatic differentiation (AD), LAMP quantifies the importance of an input for an output by computing the partial derivative. LAMP separates the original data processing and the more expensive derivative computation to different processes to achieve cost-effectiveness. In addition, it allows quantifying importance for inputs related to discrete behavior, such as control flow selection. The evaluation on a set of real world programs and data sets illustrates that LAMP produces more precise and succinct provenance than program dependence based techniques, with much less overhead. Our case studies demonstrate the potential of LAMP in problem diagnosis in data engineering.

Modern software systems are becoming increasingly complex, relying on a lot of third-party library support. Library behaviors are hence an integral part of software behaviors. Analyzing them is as important as analyzing the software itself. However, analyzing libraries is highly challenging due to the lack of source code, implementation in different languages, and complex optimizations. We observe that many Java library functions provide excellent documentation, which concisely describes the functionalities of the functions. We develop a novel technique that can construct models for Java API functions by analyzing the documentation. These models are simpler implementations in Java compared to the original ones and hence easier to analyze. More importantly, they provide the same functionalities as the original functions. Our technique successfully models 326 functions from 14 widely used Java classes. We also use these models in static taint analysis on Android apps and dynamic slicing for Java programs, demonstrating the effectiveness and efficiency of our models.

This paper proposes a way of using abstract interpretation for discovering properties about array contents in programs which manipulate arrays by sequential traversal. The method summarizes an array property as a universally quantified property. It directly treats invariant properties (including universally quantified formulas and atomic formulas) as abstract domains. Our method is sound and converges in finite time, and it is flexible. The method has been used to automatically discover nontrivial invariants for several examples. In particular, the method can represent and process multidimensional array properties.

Precondition calculation is a fundamental program verification technique. Many previous works tried to solve this problem, but ended with limited capability due to loop statements. We conducted a survey on loops manipulating commonly-used data structures occurring in several real-world open-source programs, and found that about 80% of such loops iterate over elements of a data structure, indicating that automatic calculation of preconditions with respect to post-conditions of these loops would cover a great number of real-world programs and greatly ease code verification tasks. In this paper, we specify the execution effect of a program statement using the memories modified by the statement and the new values stored in these memories after executing the statement. Thus, conditional statements and loop statements can be uniformly reduced to a sequence of assignments. Also we present an approach to calculate preconditions with respect to given post-conditions of various program statements including loops that iterate over elements of commonly-used data structures (e.g., acyclic singly-linked lists) based on execution effects of these statements. With execution effects, post-conditions and loop invariants can also be generated. Our approach handles various types of data including numeric, boolean, arrays and user-defined structures. We have implemented the approach and integrated it into the code verification tool, Accumulator. We also evaluated the approach with a variety of programs, and the results show that our approach is able to calculate preconditions for different kinds of post-conditions, including linear ones and universally quantified ones. Preconditions generated with our approach can ease the verification task by reducing the burden of providing loop invariants and preconditions of loop statements manually, which improves the automatic level and efficiency, and makes the verification less error-prone.

This paper proposes a framework facilitating the analysis on inductively defined properties for recursive data structures. Our work has three main parts. First, it helps simplify the analysis of heap-manipulating programs by classifying inductive properties of recursive data structures into two classifications and each of them is handled with observed patterns. Second, we propose a technique called slicing and splicing to track and specify how data structures are manipulated by programs, in which data structures are first sliced into several parts and these parts are further spliced into new data structures. Third, this work presents a property-directed interprocedural analysis, together with an algorithm to check the boundaries of modified procedure-local heaps regarding the recursive data structures pointed to by the parameters passed to the procedures.

The technique presented in this paper concerns the problem of how to automatically obtaining the specification of the resulting set of reachable nodes, after destructive operations over the data structure. Our work has two main contributions. First, we represent the node sets by expressions at pre-states, which facilitates the process of proving complex formulas efficiently. Second, we propose an improved version of the fold/unfold technique, which handles more general cases. Our approach is based on the observation that structure-update statements manipulate recursive data structures in a slice-and-splice way: the original data structures are sliced into several disjoint segments and these segments are further spliced into new data structures. Case studies on typical data structures show that our approach is effective and practical.