Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering最新文献_第4页

Detecting Inconsistencies in If-Condition-Raise Statements 检测If-Condition-Raise语句中的不一致性

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559514

Islem Bouzenia

{"title":"Detecting Inconsistencies in If-Condition-Raise Statements","authors":"Islem Bouzenia","doi":"10.1145/3551349.3559514","DOIUrl":"https://doi.org/10.1145/3551349.3559514","url":null,"abstract":"Developers use exceptions guarded by conditions to abort the execution when a program reaches an unexpected state. However, sometimes the condition and the raised exception do not imply the same stopping reason, in which case, we call them inconsistent if-condition-raise statements. The inconsistency can originate from a mistake in the condition or the exception message. This paper presents IICR-Finder, a deep learning-based approach to detect inconsistent if-condition-raise statements. The approach reasons both about the condition’s logic and the natural language of the exception message and raises a warning in case of inconsistency. We present six techniques to automatically generate large numbers of inconsistent statements to train two neural models based on binary classification and triplet loss. We apply the approach to 210K if-condition-raise statements extracted from 42 million lines of Python code. It achieves a precision of 72% at a recall of 60% on a dataset of past bug fixes. Running IICR-Finder on open-source projects reveals 30 previously unknown bugs, ten of which we reported, with eight confirmed by the developers.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126662685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HyperAST: Enabling Efficient Analysis of Software Histories at Scale HyperAST:实现大规模软件历史的有效分析

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3560423

Quentin Le Dilavrec, D. Khelladi, Arnaud Blouin, J. Jézéquel

{"title":"HyperAST: Enabling Efficient Analysis of Software Histories at Scale","authors":"Quentin Le Dilavrec, D. Khelladi, Arnaud Blouin, J. Jézéquel","doi":"10.1145/3551349.3560423","DOIUrl":"https://doi.org/10.1145/3551349.3560423","url":null,"abstract":"Abstract Syntax Trees (ASTs) are widely used beyond compilers in many tools that measure and improve code quality, such as code analysis, bug detection, mining code metrics, refactoring. With the advent of fast software evolution and multistage releases, the temporal analysis of an AST history is becoming useful to understand and maintain code. However, jointly analyzing thousands versions of ASTs independently faces scalability issues, mostly combinatorial, both in terms of memory and CPU usage. In this paper, we propose a novel type of AST, called HyperAST, that enables efficient temporal code analysis on a given software history by: 1/ leveraging code redundancy through space (between code elements) and time (between versions); 2/ reusing intermediate computation results. We show how the HyperAST can be built incrementally on a set of commits to capture all multiple ASTs at once in an optimized way. We evaluated the HyperAST on a curated list of large software projects. Compared to Spoon, a state-of-the-art technique, we observed that the HyperAST outperforms it with an order-of-magnitude difference from × 6 up to × 8076 in CPU construction time and from × 12 up to × 1159 in memory footprint. While the HyperAST requires up to 2 h 22 min and 7.2 GB for the biggest project, Spoon requires up to 93 h and 31 min and 2.2 TB. The gains in construction time varied from to and the gains in memory footprint varied from to . We further compared the task of finding references of declarations with the HyperAST and Spoon. We observed on average precision and recall without a significant difference in search time.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121521066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Detecting Blocking Errors in Go Programs using Localized Abstract Interpretation 利用局部抽象解释检测围棋程序中的阻塞错误

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3561154

Oskar Haarklou Veileborg, Georgian-Vlad Saioc, Anders Møller

引用次数: 3

Towards Agent-Based Testing of 3D Games using Reinforcement Learning 基于智能体的3D游戏强化学习测试

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3560507

Raihana Ferdous, Fitsum Meshesha Kifetew, D. Prandi, A. Susi

引用次数: 1

‘Who built this crap?’ Developing a Software Engineering Domain Specific Toxicity Detector “谁造的这个垃圾?”开发软件工程领域特定毒性检测器

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559508

Jaydeb Sarker

引用次数: 1

Unsupervised Summarization of Privacy Concerns in Mobile Application Reviews 移动应用程序审查中隐私问题的无监督总结

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3561155

Fahime Ebrahimi, Anas Mahmoud

{"title":"Unsupervised Summarization of Privacy Concerns in Mobile Application Reviews","authors":"Fahime Ebrahimi, Anas Mahmoud","doi":"10.1145/3551349.3561155","DOIUrl":"https://doi.org/10.1145/3551349.3561155","url":null,"abstract":"The proliferation of mobile applications (app) over the past decade has imposed unprecedented challenges on end-users privacy. Apps constantly demand access to sensitive user information in exchange for more personalized services. These—mostly unjustifiable—data collection tactics have raised major privacy concerns among mobile app users. Such concerns are commonly expressed in mobile app reviews, however, they are typically overshadowed by more generic categories of user feedback, such as app reliability and usability. This makes extracting user privacy concerns manually, or even using automated tools, a challenging and time-consuming task. To address these challenges, in this paper, we propose an effective unsupervised approach for summarizing user privacy concerns in mobile app reviews. Our analysis is conducted using a dataset of 2.6 million app reviews sampled from three different application domains. The results show that users in different application domains express their privacy concerns using domain-specific vocabulary. This domain knowledge can be leveraged to help unsupervised automated text summarization algorithms to generate concise and comprehensive summaries of privacy concerns in app review collections. Our analysis is intended to help app developers quickly and accurately identify the most critical privacy concerns in their domain of operation, and ultimately, alter their data collection practices to address these concerns.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114083591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Outcome-Preserving Input Reduction for Scientific Data Analysis Workflows 保留结果的科学数据分析工作流输入减少

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559558

Anh Duc Vu, Timo Kehrer, Christos Tsigkanos

{"title":"Outcome-Preserving Input Reduction for Scientific Data Analysis Workflows","authors":"Anh Duc Vu, Timo Kehrer, Christos Tsigkanos","doi":"10.1145/3551349.3559558","DOIUrl":"https://doi.org/10.1145/3551349.3559558","url":null,"abstract":"Analysis of data is the foundation of multiple scientific disciplines, manifesting in complex and diverse scientific data analysis workflows often involving exploratory analyses. Such analyses represent a particular case for traditional data engineering workflows, as results may be hard to interpret and judge whether they are correct or not, and where experimentation is a central theme. Oftentimes, there are certain aspects of a result which are suspicious and which should be further investigated to increase the trustworthiness of the workflow’s outcome. To this end, we advocate a semi-automated approach to reducing a workflow’s input data while preserving a specified outcome of interest, facilitating irregularity localization by narrowing down the search space for spotting corrupted input data or wrong assumptions made about it. We outline our vision on building engineering support for outcome-preserving input reduction within data analysis workflows, and report on preliminary results obtained from applying an early research prototype on a computational notebook taken from an online community of data scientists and machine learning practitioners.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114865914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Static Type Recommendation for Python Python静态类型推荐

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3561150

Keke Sun, Yifan Zhao, Dan Hao, Lu Zhang

引用次数: 2

Transfer learning of cars behaviors from reality to simulation applications 将汽车行为的学习从现实转移到仿真应用

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3560506

C. Paduraru, Miruna Paduraru, Andrei Blahovici

引用次数: 0

Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing 基于强化学习引导模糊的智能合约脆弱交易序列生成方法

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3560429

J. Su, Hongning Dai, Lingjun Zhao, Zibin Zheng, Xiapu Luo

{"title":"Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing","authors":"J. Su, Hongning Dai, Lingjun Zhao, Zibin Zheng, Xiapu Luo","doi":"10.1145/3551349.3560429","DOIUrl":"https://doi.org/10.1145/3551349.3560429","url":null,"abstract":"As computer programs run on top of blockchain, smart contracts have proliferated a myriad of decentralized applications while bringing security vulnerabilities, which may cause huge financial losses. Thus, it is crucial and urgent to detect the vulnerabilities of smart contracts. However, existing fuzzers for smart contracts are still inefficient to detect sophisticated vulnerabilities that require specific vulnerable transaction sequences to trigger. To address this challenge, we propose a novel vulnerability-guided fuzzer based on reinforcement learning, namely RLF, for generating vulnerable transaction sequences to detect such sophisticated vulnerabilities in smart contracts. In particular, we firstly model the process of fuzzing smart contracts as a Markov decision process to construct our reinforcement learning framework. We then creatively design an appropriate reward with consideration of both vulnerability and code coverage so that it can effectively guide our fuzzer to generate specific transaction sequences to reveal vulnerabilities, especially for the vulnerabilities related to multiple functions. We conduct extensive experiments to evaluate RLF’s performance. The experimental results demonstrate that our RLF outperforms state-of-the-art vulnerability-detection tools (e.g., detecting 8%-69% more vulnerabilities within 30 minutes).","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116249282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14