Do Automated Program Repair Techniques Repair Hard And Important Bugs?
ICSE is the premier forum for presenting and discussing the most recent and pregnant technical enquiry contributions in the field of Software Engineering. In the technical track, nosotros invite high quality submissions of technical research papers describing original and unpublished results of software applied science enquiry.
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Phone call for Papers
ICSE is the premier forum for presenting and discussing the most recent and significant technical enquiry contributions in the field of Software Engineering. In the technical runway, nosotros invite high quality submissions of technical inquiry papers describing original and unpublished results of software engineering enquiry.
Please note the following important changes for 2022:
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Point out the significance of your research contributions (see review criteria).
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Follow our open up science policy – share data and justify if you lot do non.
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Give your submission a unique title that is different from preprints and talks.
Research of Interest
ICSE welcomes submissions addressing topics beyond the full spectrum of Software Engineering science, being inclusive of quantitative, qualitative, and mixed-methods research. Topics of interest include:
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AI and software engineering, including
- Search-based software engineering
- Auto learning with and for SE
- Recommender systems
- Autonomic systems and self adaptation
- Program synthesis
- Plan repair
- Software fairness
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Testing and analysis, including
- Software testing
- Program assay
- Debugging and Mistake localization
- Programming languages
- Performance
- Mobile applications
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Software analytics, including
- Mining software repositories
- Apps and app store analysis
- Software ecosystems
- Configuration management
- Software visualization
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Software evolution, including
- Evolution and maintenance
- API design and evolution
- Release engineering and DevOps
- Software reuse
- Refactoring
- Program comprehension
- Reverse engineering
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Social aspects of software applied science, including
- Human aspects of software engineering
- Homo-computer interaction
- Distributed and collaborative software engineering
- Agile methods and software processes
- Software economic science
- Crowd-based software engineering
- Ethics in software engineering
- Dark-green and sustainable technologies
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Requirements, modeling, and design, including
- Requirements Engineering
- Privacy and Security past Design
- Modeling and Model-Driven Engineering
- Software Architecture and Design
- Variability and production lines
- Software services
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Dependability, including
- Formal methods
- Validation and Verification
- Reliability and Safety
- Privacy and Security
- Embedded and cyber-physical systems
Review Criteria
Each paper submitted to the Technical Rails will exist evaluated based on the following criteria:
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Soundness: The extent to which the paper'southward contributions and/or innovations accost its inquiry questions and are supported by rigorous application of appropriate research methods
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Significance: The extent to which the paper'due south contributions can impact the field of software engineering, and under which assumptions (if any)
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Novelty: The extent to which the contributions are sufficiently original with respect to the state-of-the-fine art
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Verifiability and Transparency: The extent to which the paper includes sufficient information to understand how an innovation works; to understand how data was obtained, analyzed, and interpreted; and how the paper supports independent verification or replication of the paper'due south claimed contributions
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Presentation: The extent to which the paper's quality of writing meets the loftier standards of ICSE, including clear descriptions, too as adequate use of the English language language, absence of major ambivalence, conspicuously readable figures and tables, and adherence to the formatting instructions provided beneath.
Reviewers will carefully consider all of these criteria during the review process, and authors should take great care in conspicuously addressing them all. The paper should conspicuously explain the claimed contributions, and how they are audio, significant, novel, and verifiable, as described above.
For more information on how the ICSE PC will interpret and use these criteria in the paper evaluation process, see the ICSE 2022 Review Process and Guidelines.
How to Submit
All authors should utilize the official "ACM Primary Article Template", every bit can be obtained from the ACM Proceedings Template page. LaTeX users should apply the sigconf choice, equally well as the review (to produce line numbers for easy reference past the reviewers) and anonymous (omitting author names) options. To that finish, the following LaTeX code can be placed at the kickoff of the LaTeX document:
\documentclass[sigconf,review,anonymous]{acmart}
\acmConference[ICSE 2022]{The 44th International Briefing on Software Engineering science}{May 21–29, 2022}{Pittsburgh, PA, USA}
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All submissions must non exceed 10 pages for the master text, inclusive of all figures, tables, appendices, etc. Two more than pages containing merely references are permitted. All submissions must be in PDF. Accustomed papers will be allowed ane actress page for the chief text of the photographic camera-fix version.
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Submissions must strictly adapt to the ACM formatting instructions. Alterations of spacing, font size, and other changes that deviate from the instructions may result in desk rejection without further review.
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By submitting to the ICSE Technical Track, authors admit that they are aware of and agree to be leap by the ACM Policy and Procedures on Plagiarism and the IEEE Plagiarism FAQ. In particular, papers submitted to ICSE 2022 must not have been published elsewhere and must not be under review or submitted for review elsewhere whilst nether consideration for ICSE 2022. Contravention of this concurrent submission policy will be deemed a serious breach of scientific ethics, and appropriate activity will be taken in all such cases. To check for double submission and plagiarism issues, the chairs reserve the right to (i) share the list of submissions with the PC Chairs of other conferences with overlapping review periods and (two) apply external plagiarism detection software, nether contract to the ACM or IEEE, to detect violations of these policies.
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The ICSE 2022 Technical Track will employ a double-anonymous review process. Thus, no submission may reveal its authors' identities. The authors must make every effort to honor the double-anonymous review process. In particular:
- Authors' names must be omitted from the submission.
- All references to the author's prior work should be in the third person.
- Authors are encouraged to title their submission differently than preprints of the authors on ArXiV or like sites. During review, authors should not publicly employ the submission title.
Further advice, guidance, and explanation about the double-anonymous review procedure can exist found in the Q&A page.
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Past submitting to the ICSE Technical Track, authors acknowledge that they conform to the authorship policy of the ACM, and the authorship policy of the IEEE.
Submissions to the Technical Track that meet the to a higher place requirements tin be made via the Technical Track submission site (https://icse2022.hotcrp.com) past the submission deadline. Any submission that does not comply with these requirements may exist desk rejected without further review.
We encourage the authors to upload their newspaper info early (and tin can submit the PDF later) to properly enter conflicts for double-anonymous reviewing. Authors are encouraged to try out the experimental SIGSOFT Submission Checker to detect violations to the formatting and double anonymous guidelines. (Mind that the tool is based on heuristics. Therefore it may miss violations, and it can raise false alarms. The requirements listed in this call for papers accept precedence over the results of the tool when deciding whether a paper meets the submission guidelines.)
Open Science Policy
The research track of ICSE 2022 is governed past the ICSE 2022 Open Scientific discipline policies. In summary, the steering principle is that all enquiry results should be attainable to the public and, if possible, empirical studies should be reproducible. In particular, we actively support the adoption of open up data and open source principles and encourage all contributing authors to disclose (anonymized and curated) data to increment reproducibility and replicability. Annotation that sharing enquiry information is non mandatory for submission or acceptance. Notwithstanding, sharing is expected to be the default, and non-sharing needs to be justified. We recognize that reproducibility or replicability is non a goal in qualitative research and that, like to industrial studies, qualitative studies ofttimes face challenges in sharing research data. For guidelines on how to report qualitative research to ensure the assessment of the reliability and credibility of research results, run into the Q&A page.
Upon submission to the enquiry rail, authors are asked
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to make their data available to the plan committee (via upload of supplemental material or a link to an anonymous repository) – and provide instructions on how to access this data in the paper; or
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to include in the paper an explanation equally to why this is non possible or desirable; and
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to indicate if they intend to make their data publicly bachelor upon acceptance.
Supplementary material can be uploaded via the HotCRP site or anonymously linked from the newspaper submission. Although PC members are not required to look at this material, nosotros strongly encourage authors to use supplementary material to provide access to anonymized data, whenever possible. Authors are asked to carefully review any supplementary cloth to ensure it conforms to the double-anonymous policy (described above). For instance, code and data repositories may be exported to remove version control history, scrubbed of names in comments and metadata, and anonymously uploaded to a sharing site to support review. One resource that may be helpful in accomplishing this task is this blog post.
Upon credence, authors have the possibility to separately submit their supplementary material to the ICSE 2022 Artifact Evaluation runway, for recognition of artifacts that are reusable, available, replicated or reproduced.
We are organizing an "Ask me Anything" (AMA) Session on Best practices for a successful ICSE paper in June for prospective authors to larn from the 2022 ICSE PC co-chairs, Arie van Deursen and Tao Xie.
This event takes place on two dates, i for each hemisphere:
- Wed 30 June, 17:00 CET (11:00 ET / 08:00 PT / 23:00 Beijing) - "Atlantic" time zones
- Thu, 01 July 08:00 CET (02:00 ET / 23:00 PT, ane day before / 14:00 Beijing) - "Pacific" time zones
To participate in the effect, please register here. Deadline for registration is June 24,
Author Response Period
ICSE 2022 will offering a 3 solar day writer response flow. In this period the authors will have the opportunity to inspect the reviews, and to reply specific questions raised by the program committee. This period is scheduled after all reviews take been completed, and serves to inform the subsequent conclusion making process. Authors will be able to come across the full reviews, including the reviewer scores as part of the author response process.
Withdrawing a Newspaper
Authors can withdraw their paper at whatsoever moment until the final conclusion has been made, through the paper submission organisation. Resubmitting the newspaper to another venue before the final conclusion has been made without withdrawing from ICSE 2022 kickoff is considered a violation of the concurrent submission policy, and volition pb to automatic rejection from ICSE 2022 as well equally any other venue adhering to this policy.
Important Dates
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Technical Track Abstruse Submissions (Required) Deadline: August 27, 2022
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Technical Track Submissions Borderline: September 3, 2022
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Technical Track Writer Response Menstruation: November 10–13, 2022
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Technical Runway Credence Notification: December three, 2022
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Technical Track Camera Set: Feb eleven, 2022
Conference Attendance Expectation
If a submission is accustomed, at least one writer of the newspaper is required to register for ICSE 2022 and nowadays the newspaper. [We will add more info on this as shortly every bit the ICSE 2022 format is finalized.]
Accepted Papers
| Title | |
|---|---|
| Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching Technical Track Zhuangbin Chen, Jinyang Liu, Yuxin Su, Hongyu Zhang, Xiao Ling, Yongqiang Yang, Michael Lyu | |
| Adaptive Examination Selection for Deep Neural Networks Technical Track Xinyu Gao, Yang Feng, Yining Yin, Zixi Liu, Zhenyu Chen, Baowen Xu | |
| μAFL: Not-intrusive Feedback-driven Fuzzing for Microcontroller Firmware Technical Track Wenqiang Li, Jiameng Shi, Fengjun Li, Jingqiang Lin, Wei Wang, Le Guan | |
| A Grounded Theory Based Arroyo to Characterize Software Attack Surfaces Technical Track sara moshtari, Ahmet Okutan, Mehdi Mirakhorli | |
| A Grounded Theory of Coordination in Remote-Showtime and Hybrid Software Teams Technical Track Ronnie E. de Souza Santos, Paul Ralph | |
| Analyzing User Perspectives on Mobile App Privacy at Calibration Technical Track Preksha Nema, Pauline Anthonysamy, Nina Taft, Sai Teja Peddinti | |
| An Exploratory Study of Deep Learning Supply Concatenation Technical Track Xin Tan, Kai Gao, Minghui Zhou, Li Zhang | |
| An Exploratory Report of Productivity in Software Teams Technical Rail Anastasia Ruvimova, Alexander Lill, Gail Murphy, Elaine Huang, Jan Gugler, Lauren Howe, Thomas Fritz | |
| Aper: Evolution-Enlightened Runtime Permission Misuse Detection for Android Apps Technical Track Sinan Wang, Yibo Wang, Xian Zhan, Ying Wang, Yepang Liu, Xiapu Luo, Shing-Chi Cheung | |
| ARCLIN: Automated API Mention Resolution for Unformatted Texts Technical Track Yintong Huo, Yuxin Su, Hongming Zhang, Michael Lyu | |
| A Scalable t-wise Coverage Reckoner Technical Rail Eduard Baranov, Sourav Chakraborty, Axel Legay, Kuldeep S. Meel, North. V. Vinodchandran | |
| AST-Trans: Code Summarization with Efficient Tree-Structured Attention Technical Rails Ze Tang, Xiaoyu Shen, Chuanyi Li, Jidong Ge, Liguo Huang, Zheling Zhu, Bin Luo | |
| A Universal Data Augmentation Arroyo for Fault Localization Technical Track Huan Xie, Yan Lei, Meng Yan, Yue Yu, Xin Xia, Xiaoguang Mao | |
| Automated Assertion Generation via Data Retrieval and Its Integration with Deep Learning Technical Track Hao Yu, Yiling Lou, Ke Sunday, Dezhi Ran, Tao Xie, Dan Hao, Ying Li, Ge Li, Qianxiang Wang | |
| Automatic Detection of Password Leakage from Public GitHub Repositories Technical Rails Runhan Feng, Ziyang Yan, Shiyan Peng, Yuanyuan Zhang | |
| Automatic Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Report Technical Rails Saad Ezzini, Sallam Abualhaija, Chetan Arora, Mehrdad Sabetzadeh | |
| Automated Patching for Unreproducible Builds Technical Track Zhilei Ren, Shiwei Lord's day, Jifeng Xuan, Xiaochen Li, Zhide Zhou, He Jiang | |
| Automated Testing of Software that Uses Car Learning APIs Technical Track Chengcheng Wan, Shicheng Liu, Sophie Xie, Yifan Liu, Henry Hoffmann, Michael Maire, Shan Lu | |
| Automatic Detection of Operation Bugs in Database Systems using Equivalent Queries Technical Track Xinyu Liu, Qi Zhou, Joy Arulraj, Alessandro Orso | |
| AutoTransform: Automated Code Transformation to Support Modern Code Review Process Technical Runway Patanamon Thongtanunam, Chanathip Pornprasit, Chakkrit Tantithamthavorn | |
| BeDivFuzz: Integrating Behavioral Diverseness into Generator-based Fuzzing Technical Track Hoang Lam Nguyen, Lars Grunske | |
| Large Data = Big Insights? Operationalizing Brooks' Law in a Massive GitHub Data Set Technical Rail Christoph Gote, Pavlin Mavrodiev, Frank Schweitzer, Ingo Scholtes | |
| Bots for Pull Requests: The Good, the Bad, and the Promising Technical Rail Mairieli Wessel, Ahmad Abdellatif, Igor Wiese, Tayana Conte, Emad Shihab, Marco Gerosa, Igor Steinmacher | |
| Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding Technical Rail Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, Xiangke Liao | |
| BugListener: Identifying and Synthesizing Issues Reports from Collaborative Alive Chats Technical Track Lin Shi, Fangwen Mu, YuMin Zhang, Ye Yang, Junjie Chen, Xiao Chen, Hanzhi Jiang, Ziyou Jiang, Qing Wang | |
| BuildSheriff: Change-Aware Test Failure Triage for Continuous Integration Builds Technical Track Chen Zhang, Bihuan Chen, Xin Peng, Wenyun Zhao | |
| Causality-Based Neural Network Repair Technical Runway Bing Sun, Jun Sunday, Long H. Pham, Jie Shi | |
| Causality in Configurable Software Systems Technical Rails Clemens Dubslaff, Kallistos Weis, Christel Baier, Sven Apel | |
| Alter Is the Merely Constant: Dynamic Updates for Workflows Technical Runway Daniel Sokolowski, Pascal Weisenburger, Guido Salvaneschi | |
| Characterizing and Detecting Bugs in WeChat Mini-Programs Technical Track Tao Wang, Qingxin Xu, Xiaoning Chang, Wensheng Dou, Jiaxin Zhu, Jinhui Xie, Yuetang Deng, Jianbo Yang, Jiaheng Yang, Jun Wei, Tao Huang | |
| Articulate: Contrastive Learning for API Recommendation Technical Rails Moshi Wei, Nima Shiri harzevili, Yuchao Huang, Junjie Wang, Song Wang | |
| CodeFill: Multi-token Code Completion by Jointly Learning from Construction and Naming Sequences Technical Rail Maliheh Izadi, Roberta Gismondi, Georgios Gousios | |
| Code Search based on Context-aware Code Translation Technical Track Weisong Sun, Chunrong Fang, Yuchen Chen, Guanhong Tao, Tingxu Han, Quanjun Zhang | |
| Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process Technical Track Nadia Nahar, Shurui Zhou, Grace Lewis, Christian Kästner | |
| Combinatorial Testing of RESTful APIs Technical Runway Huayao Wu, Lixin Xu, Xintao Niu, Changhai Nie | |
| CONFETTI: Amplifying Concolic Guidance for Fuzzers Technical Track James Kukucka, Luís Pina, Paul Ammann, Jonathan Bell | |
| Conflict-enlightened Inference of Python Compatible Runtime Environments with Domain Knowledge Graph Technical Track Wei Cheng, XiangRong Zhu, Wei Hu | |
| Controlled Concurrency Testing via Periodical Scheduling Technical Rails Cheng Wen, Mengda He, Bohao Wu, Zhiwu Xu, Shengchao Qin | |
| Control Parameters Considered Harmful: Detecting Range Specification Bugs in Drone Configuration Modules via Learning-Guided Search Technical Track Ruidong Han, Chao Yang, Siqi Ma, Jianfeng Ma, Cong Sun, Juanru Li, Elisa Bertino | |
| Cross-Domain Deep Code Search with Few-Shot Learning Technical Track Yitian Chai, Hongyu Zhang, Beijun Shen, Xiaodong Gu | |
| Data-Driven Loop Bound Learning for Termination Analysis Technical Track Rongchen Xu, Jianhui Chen, Fei He | |
| DEAR: A Novel Deep Learning-based Arroyo for Automated Program Repair Technical Track Yi Li, Shaohua Wang, Tien N. Nguyen | |
| Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules Technical Track Rangeet Pan, Hridesh Rajan | |
| Decomposing Software Verification into Off-the-Shelf Components: An Application to CEGAR Technical Rail Dirk Beyer, Jan Haltermann, Thomas Lemberger, Heike Wehrheim | |
| DeepAnalyze: Learning to Localize Crashes at Scale Technical Track Manish Shetty, Chetan Bansal, Suman Nath, Sean Bowles, Henry Wang, Ozgur Arman, Siamak Ahari | |
| DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs Technical Track Mohammad Wardat, Breno Dantas Cruz, Wei Le, Hridesh Rajan | |
| DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs Technical Track Jialun Cao, Meiziniu LI, Xiao Chen, Ming Wen, Yongqiang Tian, Bo Wu, Shing-Chi Cheung | |
| DeepStability: A Written report of Unstable Numerical Methods and Their Solutions in Deep Learning Technical Track Eliska Kloberdanz, Kyle Kloberdanz, Wei Le | |
| DeepState: Selecting Examination Suites to Heighten the Robustness of Recurrent Neural Networks Technical Rails Zixi Liu, Yang Feng, Yining Yin, Zhenyu Chen | |
| DeepSTL - From English Requirements to Signal Temporal Logic Technical Rail Jie He, Ezio Bartocci, Dejan Nickovic, Haris Isakovic, Radu Grosu | |
| DeepTraLog: Trace-Log Combined Microservice Bibelot Detection through Graph-based Deep Learning Technical Rails Chenxi Zhang, Xin Peng, Chaofeng Sha, Ke Zhang, Zhenqing Fu, Xiya Wu, Qingwei Lin, Dongmei Zhang | |
| DeFault: Common Information-based Crash Triage for Massive Crashes Technical Track Xing Zhang, Jiongyi Chen, Chao Feng, Ruilin Li, Wenrui Diao, Kehuan Zhang | |
| Demystifying Android Non-SDK APIs: Measurement and Agreement Technical Track Shishuai Yang, Rui Li, Jiongyi Chen, Wenrui Diao, Shanqing Guo | |
| Demystifying the Dependency Challenge in Kernel Fuzzing Technical Track Yu Hao, Hang Zhang, Guoren Li, Xingyun Du, Zhiyun Qian, Ardalan Sani | |
| Demystifying the Vulnerability Propagation and Its Evolution via Dependency Trees in the NPM Ecosystem Technical Track Chengwei Liu, Sen Chen, Lingling Fan, Bihuan Chen, Yang Liu, Xin Peng | |
| DescribeCtx: Context-Aware Description Synthesis for Sensitive Behaviors in Mobile Apps Technical Track Shao Yang, Yuehan Wang, Yuan Yao, Haoyu Wang, Yanfang Ye, Xusheng Xiao | |
| Detecting Faux Alarms from Automatic Static Analysis Tools: How Far are We? Technical Track Hong Jin Kang, Khai Loong Aw, David Lo | |
| "Did You Miss My Annotate or What?" Understanding Toxicity in Open up Source Discussions Technical Rails Courtney Miller, Sophie Cohen, Daniel Klug, Bogdan Vasilescu, Christian Kästner | |
| Difuzer: Uncovering Suspicious Hidden Sensitive Operations in Android Apps Technical Rail Jordan Samhi, Li Li, Tegawendé F. Bissyandé, Jacques Klein | |
| Discovering Repetitive Code Changes in Python ML Systems Technical Rail Malinda Dilhara, Ameya Ketkar, Nikhith Sannidhi, Danny Dig | |
| Diverseness-Driven Automated Formal Verification Technical Rails Emily Offset, Yuriy Brun | |
| Domain-Specific Analysis of Mobile App Reviews Using Keyword-Assisted Topic Models Technical Runway Miroslav Tushev, Fahimeh Ebrahimi, Anas "Nash" Mahmoud | |
| DrAsync: Identifying and Visualizing Anti-Patterns in Asynchronous JavaScript Technical Track Alexi Turcotte, Michael D. Shah, Mark W. Aldrich, Frank Tip | |
| Dynamic Update for Synthesized GR(1) Controllers Technical Track Gal Amram, Shahar Maoz, Itai Segall, Matan Yossef | |
| EAGLE: Creating Equivalent Graphs to Test Deep Learning Libraries Technical Track Jiannan Wang, Thibaud Lutellier, Shangshu Qian, Viet Hung Pham, Lin Tan | |
| Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and Many-Objective Optimization Technical Rails Fitash Ul Haq, Donghwan Shin, Lionel Briand | |
| Eflect: Porting Energy-Enlightened Applications to Shared Environments Technical Track Timur Babakol, Anthony Canino, Yu David Liu | |
| EREBA: Black-box Energy Testing of Adaptive Neural Networks Technical Track Mirazul Haque, Yaswanth Yadlapalli, Wei Yang, Cong Liu | |
| Evaluating and Improving Neural Programme-Smoothing-based Fuzzing Technical Rails Mingyuan Wu, Ling Jiang, Jiahong Xiang, Yuqun Zhang, Guowei Yang, Huixin Ma, Sen Nie, Shi Wu, Heming Cui, Lingming Zhang | |
| ExAIS: Executable AI Semantics Technical Rail Richard Schumi, Jun Lord's day | |
| Explanation-Guided Fairness Testing through Genetic Algorithm Technical Rail Ming Fan, Wenying Wei, Wuxia Jin, Zijiang Yang, Ting Liu | |
| Exploiting Input Sanitization for Regex Denial of Service Technical Track Efe Barlas, Xin Du, James C. Davis | |
| FADATest: Fast and Adaptive Performance Regression Testing of Dynamic Binary Translation Systems Technical Track Jin Wu, Jian Dong, Ruili Fang, Wen Zhang, Wenwen Wang, Decheng Zuo | |
| Fairness-aware Configuration of Machine Learning Libraries Technical Track Saeid Tizpaz-Niari, Ashish Kumar, Gang Tan, Ashutosh Trivedi | |
| FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons Technical Rail Xuanqi Gao, Juan Zhai, Shiqing Ma, Chao Shen, Yufei Chen, Qian Wang | |
| Fast and Precise Application Lawmaking Analysis using a Fractional Library Technical Track Akshay Utture, Jens Palsberg | |
| Fast Changeset-based Bug Localization with BERT Technical Runway Agnieszka Ciborowska , Kostadin Damevski | |
| Mistake Localization via Efficient Probabilistic Modeling of Programme Semantics Technical Track Muhan Zeng, Yiqian Wu, Zhentao Ye, Yingfei Xiong, Xin Zhang, Lu Zhang | |
| FIRA: Fine-Grained Graph-Based Lawmaking Change Representation for Automatic Commit Message Generation Technical Rails Jinhao Dong, Yiling Lou, Qihao Zhu, Zeyu Dominicus, Zhilin Li, Wenjie Zhang, Dan Hao | |
| FlakiMe: Laboratory-Controlled Test Flakiness Impact Cess Technical Runway Maxime Cordy, Renaud Rwemalika, Adriano Franci, Mike Papadakis, Mark Harman | |
| Gratis Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source Technical Track Anjiang Wei, Yinlin Deng, Chenyuan Yang, Lingming Zhang | |
| Fuzzing Class Specifications Technical Track Facundo Molina, Marcelo d'Amorim, Nazareno Aguirre | |
| Garbage Drove Makes Rust Easier to Employ: A Randomized Controlled Trial of the Statuary Garbage Collector Technical Track Michael Coblenz, Michelle Mazurek, Michael Hicks | |
| Generating and Visualizing Trace Link Explanations Technical Track Yalin Liu, Jinfeng Lin, Oghenemaro Anuyah, Ronald Metoyer, Jane Cleland-Huang | |
| GIFdroid: Automated Replay of Visual Bug Reports for Android Apps Technical Track Sidong Feng, Chunyang Chen | |
| GitHub Sponsors: Exploring a New Way to Contribute to Open Source Technical Track Naomichi Shimada, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto | |
| GraphFuzz: Library API Fuzzing with Lifetime-enlightened Dataflow Graphs Technical Track Harrison Green, Thanassis Avgerinos | |
| Green AI: Do Deep Learning Frameworks Have Different Costs? Technical Runway Stefanos Georgiou, Maria Kechagia, Tushar Sharma, Federica Sarro, Ying Zou | |
| Guidelines for Assessing the Accuracy of Log Message Template Identification Techniques Technical Track Zanis Ali Khan, Donghwan Shin, Domenico Bianculli, Lionel Briand | |
| Hashing It Out: A Survey of Programmers' Cannabis Usage, Perception, and Motivation Technical Track Madeline Endres, Kevin Boehnke, Westley Weimer | |
| Hiding Critical Program Components via Ambiguous Translation Technical Track Chijung Jung, Doowon Kim, An Chen, Weihang Wang, Yunhui Zheng, Kyu Hyung Lee, Yonghwi Kwon | |
| History-Driven Test Plan Synthesis for JVM Testing Technical Track Yingquan Zhao, Zan Wang, Junjie Chen, Mengdi Liu, Mingyuan Wu, Yuqun Zhang, Lingming Zhang | |
| If a Human Tin See Information technology, And so Should Your Organization: Reliability Requirements for Motorcar Vision Components Technical Track Boyue Caroline Hu, Lina Marsso, Krzysztof Czarnecki, Rick Salay, Huakun Shen, Marsha Chechik | |
| Imperative versus Declarative Collection Processing: An RCT on the Understandability of Traditional Loops versus the Stream API in Java Technical Track Nils Mehlhorn, Stefan Hanenberg | |
| Improving Fault Localization and Plan Repair with Deep Semantic Features and Transferred Knowledge Technical Track Xiangxin Meng, Xu Wang, Hongyu Zhang, Hailong Sun, Xudong Liu | |
| Improving Machine Translation Systems via Isotopic Replacement Technical Track Zeyu Sun, Jie M. Zhang, Yingfei Xiong, Mark Harman, Mike Papadakis, Lu Zhang | |
| Inference and Test Generation Using Programme Invariants in Chemical Reaction Networks Technical Track Michael C. Gerten, Alexis L. Marsh, James I. Lathrop, Myra Cohen, Andrew S. Miner, Titus H. Klinge | |
| Inferring And Applying Type Changes Technical Track Ameya Ketkar, Oleg Smirnov, Nikolaos Tsantalis, Danny Dig, Timofey Bryksin | |
| Jigsaw: Big Linguistic communication Models meet Program Synthesis Technical Track Naman Jain, Skanda Vaidyanath, Arun Iyer, Nagarajan Natarajan, Suresh Parthasarathy, Sriram Rajamani, Rahul Sharma | |
| JuCify: A Step Towards Android Code Unification for Enhanced Static Analysis Technical Rails Jordan Samhi, Jun Gao, Nadia Daoudi, Pierre Graux, Henri Hoyez, Xiaoyu Sunday, Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein | |
| Noesis-Based Environment Dependency Inference for Python Programs Technical Track Hongjie Ye, Wei Chen, Wensheng Dou, Guoquan Wu, Jun Wei | |
| Large-scale Security Measurements on the Android Firmware Ecosystem Technical Track Qinsheng Hou, Wenrui Diao, Yanhao Wang, Xiaofeng Liu, Song Liu, Lingyun Ying, Shanqing Guo, Yuanzhi Li, Meining Nie, Haixin Duan | |
| Learning and Programming Challenges of Rust: A Mixed-Methods Study Technical Track Shuofei Zhu, Ziyi Zhang, Boqin Qin, Aiping Xiong, Linhai Song | |
| Learning Probabilistic Models for Static Assay Alarms Technical Track Hyunsu Kim, Mukund Raghothaman, Kihong Heo | |
| Learning to Recommend Method Names with Global Context Technical Track Fang Liu, Ge Li, Zhiyi Fu, Shuai Lu, Yiyang Hao, Zhi Jin | |
| Learning to Reduce False Positives in Analytic Bug Detectors Technical Track Anant Kharkar, Roshanak Zilouchian Moghaddam, Matthew Jin, Xiaoyu Liu, Xin Shi, Colin Clement, Neel Sundaresan | |
| Less is More: Supporting Developers in Vulnerability Detection during Code Review Technical Rail Larissa Braz, Christian Aeberhard, Gül Calikli, Alberto Bacchelli | |
| Lessons from Eight Years of Operational Information from a Continuous Integration Service: A Case Study of CircleCI Technical Track Keheliya Gallaba, Maxime Lamothe, Shane McIntosh | |
| Linear-time Temporal Logic guided Greybox Fuzzing Technical Track Ruijie Meng, Zhen Dong, Jialin Li, Ivan Beschastnikh, Abhik Roychoudhury | |
| Log-based Anomaly Detection with Deep Learning: How Far Are We Technical Runway Van-Hoang Le, Hongyu Zhang | |
| Manas: Mining Software Repositories to Assist AutoML Technical Track Giang Nguyen, Md Johirul Islam, Rangeet Pan, Hridesh Rajan | |
| Modeling Review History for Reviewer Recommendation: A Hypergraph Approach Technical Track Guoping Rong, YiFan Zhang, Lanxin Yang, Fuli Zhang, Hongyu Kuang, He Zhang | |
| Modx: Binary Level Fractional Imported Tertiary-Party Library Detection through Programme Modularization and Semantic Matching Technical Runway Can Yang, Zhengzi Xu, Hongxu Chen, Yang Liu, Xiaorui Gong, Baoxu Liu | |
| MOREST: Model-based RESTful API Testing with Execution Feedback Technical Track YI LIU, Yuekang Li, Gelei Deng, Yang Liu, Ruiyuan Wan, Runchao Wu, Dandan Ji, Shiheng Xu, Minli Bao | |
| Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing Technical Runway Jiazhen Gu, Xuchuan Luo, Yangfan Zhou, Xin Wang | |
| Multi-Intention-Aware Configuration Selection for Performance Tuning Technical Track Haochen He, Zhouyang Jia, Shanshan Li, Yue Yu, Chenglong Zhou, Qing Liao, Ji Wang, Xiangke Liao | |
| Multilingual training for Software Engineering Technical Track Toufique Ahmed, Prem Devanbu | |
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Do Automated Program Repair Techniques Repair Hard And Important Bugs?,
Source: https://conf.researchr.org/track/icse-2022/icse-2022-papers
Posted by: madrigalhiscon.blogspot.com
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