2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)最新文献_第6页

Semantic Image Fuzzing of AI Perception Systems 人工智能感知系统的语义图像模糊

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510212

Trey Woodlief, Sebastian G. Elbaum, Kevin Sullivan

{"title":"Semantic Image Fuzzing of AI Perception Systems","authors":"Trey Woodlief, Sebastian G. Elbaum, Kevin Sullivan","doi":"10.1145/3510003.3510212","DOIUrl":"https://doi.org/10.1145/3510003.3510212","url":null,"abstract":"Perception systems enable autonomous systems to interpret raw sensor readings of the physical world. Testing of perception systems aims to reveal misinterpretations that could cause system failures. Current testing methods, however, are inadequate. The cost of human interpretation and annotation of real-world input data is high, so manual test suites tend to be small. The simulation-reality gap reduces the validity of test results based on simulated worlds. And methods for synthesizing test inputs do not provide corresponding expected interpretations. To address these limitations, we developed semSensFuzz, a new approach to fuzz testing of perception systems based on semantic mutation of test cases that pair realworld sensor readings with their ground-truth interpretations. We implemented our approach to assess its feasibility and potential to improve software testing for perception systems. We used it to generate 150,000 semantically mutated image inputs for five state-of-the-art perception systems. We found that it synthesized tests with novel and subjectively realistic image inputs, and that it discovered inputs that revealed significant inconsistencies between the specified and computed interpretations. We also found that it produced such test cases at a cost that was very low compared to that of manual semantic annotation of real-world images.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124786362","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: 8

Learning Probabilistic Models for Static Analysis Alarms 静态分析报警的学习概率模型

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510098

Hyunsu Kim, Mukund Raghothaman, K. Heo

引用次数: 3

FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation FIRA:用于自动提交消息生成的细粒度基于图的代码更改表示

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510069

Jinhao Dong, Yiling Lou, Qihao Zhu, Zeyu Sun, Zhilin Li, Wenjie Zhang, Dan Hao

{"title":"FIRA: Fine-Grained Graph-Based Code Change Representation for Automated Commit Message Generation","authors":"Jinhao Dong, Yiling Lou, Qihao Zhu, Zeyu Sun, Zhilin Li, Wenjie Zhang, Dan Hao","doi":"10.1145/3510003.3510069","DOIUrl":"https://doi.org/10.1145/3510003.3510069","url":null,"abstract":"Commit messages summarize code changes of each commit in nat-ural language, which help developers understand code changes without digging into detailed implementations and play an essen-tial role in comprehending software evolution. To alleviate human efforts in writing commit messages, researchers have proposed var-ious automated techniques to generate commit messages, including template-based, information retrieval-based, and learning-based techniques. Although promising, previous techniques have limited effectiveness due to their coarse-grained code change representations. This work proposes a novel commit message generation technique, FIRA, which first represents code changes via fine-grained graphs and then learns to generate commit messages automati-cally. Different from previous techniques, FIRA represents the code changes with fine-grained graphs, which explicitly describe the code edit operations between the old version and the new version, and code tokens at different granularities (i.e., sub-tokens and integral tokens). Based on the graph-based representation, FIRA generates commit messages by a generation model, which includes a graph-neural-network-based encoder and a transformer-based decoder. To make both sub-tokens and integral tokens as available ingredients for commit message generation, the decoder is further incorporated with a novel dual copy mechanism. We further per-form an extensive study to evaluate the effectiveness of FIRA. Our quantitative results show that FIRA outperforms state-of-the-art techniques in terms of BLEU, ROUGE-L, and METEOR; and our ablation analysis further shows that major components in our technique both positively contribute to the effectiveness of FIRA. In addition, we further perform a human study to evaluate the quality of generated commit messages from the perspective of developers, and the results consistently show the effectiveness of FIRA over the compared techniques.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129492735","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: 24

Lessons from Eight Years of Operational Data from a Continuous Integration Service: An Exploratory Case Study of CircleCI 持续集成服务八年运行数据的经验教训:CircleCI的探索性案例研究

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510211

Keheliya Gallaba, Maxime Lamothe, Shane McIntosh

{"title":"Lessons from Eight Years of Operational Data from a Continuous Integration Service: An Exploratory Case Study of CircleCI","authors":"Keheliya Gallaba, Maxime Lamothe, Shane McIntosh","doi":"10.1145/3510003.3510211","DOIUrl":"https://doi.org/10.1145/3510003.3510211","url":null,"abstract":"Continuous Integration (CI) is a popular practice that enables the rapid pace of modern software development. Cloud-based CI services have made CI ubiquitous by relieving software teams of the hassle of maintaining a CI infrastructure. To improve these CI services, prior research has focused on analyzing historical CI data to help service consumers. However, finding areas of improvement for CI service providers could also improve the experience for service consumers. To search for these opportunities, we conduct an empirical study of 22.2 million builds spanning 7,795 open-source projects that used CircleCI from 2012 to 2020. First, we quantitatively analyze the builds (i.e., invocations of the CI service) with passing or failing outcomes. We observe that the heavy and typical service consumer groups spend significantly different proportions of time on seven of the nine build actions (e.g., dependency retrieval). On the other hand, the compilation and testing actions consistently consume a large proportion of build time across consumer groups (median 33%). Second, we study builds that terminate prior to generating a pass or fail signal. Through a systematic manual analysis, we find that availability issues, configuration errors, user cancellation, and exceeding time limits are key reasons that lead to premature build termination. Our observations suggest that (1) heavy service consumers would benefit most from build acceleration approaches that tackle long build durations (e.g., skipping build steps) or high throughput rates (e.g., optimizing CI service job queues), (2) efficiency in CI pipelines can be improved for most CI consumers by focusing on the compilation and testing stages, and (3) avoiding misconfigurations and tackling service availability issues present the largest opportunities for improving the robustness of CI services.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125377822","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: 10

DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs 深度学习程序的自动故障诊断和定位

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510099

Jialun Cao, Meiziniu Li, Xiao Chen, Ming Wen, Yongqiang Tian, Bo Wu, S. Cheung

{"title":"DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs","authors":"Jialun Cao, Meiziniu Li, Xiao Chen, Ming Wen, Yongqiang Tian, Bo Wu, S. Cheung","doi":"10.1145/3510003.3510099","DOIUrl":"https://doi.org/10.1145/3510003.3510099","url":null,"abstract":"As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network (DNN), which, unfortunately, might be a detour. Specifically, several existing studies have reported that many unsatisfactory behaviors are actually originated from the faults residing in DL programs. Besides, locating faulty neurons is not actionable for developers, while locating the faulty statements in DL programs can provide developers with more useful information for debugging. Though a few recent studies were proposed to pinpoint the faulty statements in DL programs or the training settings (e.g. too large learning rate), they were mainly designed based on predefined rules, leading to many false alarms or false negatives, especially when the faults are beyond their capabilities. In view of these limitations, in this paper, we proposed DeepFD, a learning-based fault diagnosis and localization framework which maps the fault localization task to a learning problem. In particu-lar, it infers the suspicious fault types via monitoring the runtime features extracted during DNN model training, and then locates the diagnosed faults in DL programs. It overcomes the limitations by identifying the root causes of faults in DL programs instead of neurons, and diagnosing the faults by a learning approach instead of a set of hard-coded rules. The evaluation exhibits the potential of DeepFD. It correctly diagnoses 52% faulty DL programs, compared with around half (27%) achieved by the best state-of-the-art works. Besides, for fault localization, DeepFD also outperforms the existing works, correctly locating 42% faulty programs, which almost doubles the best result (23%) achieved by the existing works.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128768327","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: 16

Improving Fault Localization and Program Repair with Deep Semantic Features and Transferred Knowledge 利用深度语义特征和迁移知识改进故障定位和程序修复

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510147

Xiangxin Meng, Xu Wang, Hongyu Zhang, Hailong Sun, Xudong Liu

{"title":"Improving Fault Localization and Program Repair with Deep Semantic Features and Transferred Knowledge","authors":"Xiangxin Meng, Xu Wang, Hongyu Zhang, Hailong Sun, Xudong Liu","doi":"10.1145/3510003.3510147","DOIUrl":"https://doi.org/10.1145/3510003.3510147","url":null,"abstract":"Automatic software debugging mainly includes two tasks of fault lo-calization and automated program repair. Compared with the traditional spectrum-based and mutation-based methods, deep learning-based methods are proposed to achieve better performance for fault localization. However, the existing methods ignore the deep seman-tic features or only consider simple code representations. They do not leverage the existing bug-related knowledge from large-scale open-source projects either. In addition, existing template-based program repair techniques can incorporate project specific information better than deep-learning approaches. However, they are weak in selecting the fix templates for efficient program repair. In this work, we propose a novel approach called TRANSFER, which lever-ages the deep semantic features and transferred knowledge from open-source data to improve fault localization and program repair. First, we build two large-scale open-source bug datasets and design 11 BiLSTM-based binary classifiers and a BiLSTM-based multi-classifier to learn deep semantic features of statements for fault localization and program repair, respectively. Second, we combine semantic-based, spectrum-based and mutation-based features and use an MLP-based model for fault localization. Third, the semantic-based features are leveraged to rank the fix templates for program repair. Our extensive experiments on widely-used benchmark De-fects4J show that TRANSFER outperforms all baselines in fault localization, and is better than existing deep-learning methods in automated program repair. Compared with the typical template-based work TBar, TRANSFER can correctly repair 6 more bugs (47 in total) on Defects4J.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128334077","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: 16

PerfSig: Extracting Performance Bug Signatures via Multi-modality Causal Analysis PerfSig:通过多模态因果分析提取性能缺陷签名

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510110

Jingzhu He, ShanghaiTech, Chin-Chia Michael Yeh

{"title":"PerfSig: Extracting Performance Bug Signatures via Multi-modality Causal Analysis","authors":"Jingzhu He, ShanghaiTech, Chin-Chia Michael Yeh","doi":"10.1145/3510003.3510110","DOIUrl":"https://doi.org/10.1145/3510003.3510110","url":null,"abstract":"Diagnosing a performance bug triggered in production cloud environments is notoriously challenging. Extracting performance bug signatures can help cloud operators quickly pinpoint the problem and avoid repeating manual efforts for diagnosing similar performance bugs. In this paper, we present PerfSig, a multi-modality performance bug signature extraction tool which can identify principal anomaly patterns and root cause functions for performance bugs. PerfSig performs fine-grained anomaly detection over various machine data such as system metrics, system logs, and function call traces. We then conduct causal analysis across different machine data using information theory method to pinpoint the root cause function of a performance bug. PerfSig generates bug signatures as the combination of the identified anomaly patterns and root cause functions. We have implemented a prototype of PerfSig and conducted evaluation using 20 real world performance bugs in six commonly used cloud systems. Our experimental results show that PerfSig captures various kinds of fine-grained anomaly patterns from different machine data and successfully identifies the root cause functions through multi-modality causal analysis for 19 out of 20 tested performance bugs.","PeriodicalId":202896,"journal":{"name":"2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126865362","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: 3

Online Summarizing Alerts through Semantic and Behavior Information 在线汇总警报通过语义和行为信息

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510055

Jia Chen, Peng Wang, Wei Wang

引用次数: 4

Dynamic Update for Synthesized GR(1) Controllers 合成GR(1)控制器的动态更新

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3510054

Gal Amram, S. Maoz, Itai Segall, Matan Yossef

引用次数: 1

Bots for Pull Requests: The Good, the Bad, and the Promising 拉请求的机器人:好的，坏的和有希望的

2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE) Pub Date : 2022-05-01 DOI: 10.1145/3510003.3512765

M. Wessel, Ahmad Abdellatif, I. Wiese, T. Conte, Emad Shihab, M. Gerosa, Igor Steinmacher

引用次数: 19