Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00078
Yigit Küçük, Tim A. D. Henderson, Andy Podgurski
This work presents an overview of the artifact for the paper titled "Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques". The artifact was implemented in a virtual machine and includes the scripts for the UniVal algorithm for fault localization employing the Defects4J test suite. Technical information about the individual components for the artifact's repository as well as guidance on the necessary documentation for utilizing the software is provided.
{"title":"Artifact for Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques","authors":"Yigit Küçük, Tim A. D. Henderson, Andy Podgurski","doi":"10.1109/ICSE-Companion52605.2021.00078","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00078","url":null,"abstract":"This work presents an overview of the artifact for the paper titled \"Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques\". The artifact was implemented in a virtual machine and includes the scripts for the UniVal algorithm for fault localization employing the Defects4J test suite. Technical information about the individual components for the artifact's repository as well as guidance on the necessary documentation for utilizing the software is provided.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132964022","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00089
Frédéric Recoules, Sébastien Bardin, Richard Bonichon, Matthieu Lemerre, L. Mounier, Marie-Laure Potet
The main goal of the artifact is to support the experimental claims of the paper #992 "Interface Compliance of Inline As-sembly: Automatically Check, Patch and Refine" by making both the prototype and data availableto the community. The expected result is the same output as the figures given in Table I and Table IV (appendix C) of the paper. In addition, we hope the released snapshot of our prototype is simple, documented and robust enough to have some uses for people dealing withinline assembly.
工件的主要目标是通过使原型和数据对社区可用来支持论文#992“Inline as - assembly的接口遵从性:自动检查、修补和改进”的实验声明。预期结果与本文表一和表四(附录C)给出的数字相同。此外，我们希望发布的原型快照是简单的，有文档记录的，并且足够健壮，可以让人们使用内联汇编。
{"title":"RUSTInA: Automatically Checking and Patching Inline Assembly Interface Compliance (Artifact Evaluation): Accepted submission #992 – “Interface Compliance of Inline Assembly: Automatically Check, Patch and Refine”","authors":"Frédéric Recoules, Sébastien Bardin, Richard Bonichon, Matthieu Lemerre, L. Mounier, Marie-Laure Potet","doi":"10.1109/ICSE-Companion52605.2021.00089","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00089","url":null,"abstract":"The main goal of the artifact is to support the experimental claims of the paper #992 \"Interface Compliance of Inline As-sembly: Automatically Check, Patch and Refine\" by making both the prototype and data availableto the community. The expected result is the same output as the figures given in Table I and Table IV (appendix C) of the paper. In addition, we hope the released snapshot of our prototype is simple, documented and robust enough to have some uses for people dealing withinline assembly.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130170710","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00090
Larissa Braz, Enrico Fregnan, G. Çalikli, Alberto Bacchelli
Improper Input Validation (IIV) is a dangerous software vulnerability that occurs when a system does not safely handle input data. Although IIV is easy to detect and fix, it still commonly happens in practice; so, why do developers not recognize IIV? Answering this question is key to understand how to support developers in creating secure software systems. In our work, we studied to what extent developers can detect IIV and investigate underlying reasons. To do so, we conducted an online experiment with 146 software developers. In this document, we explain how to obtain the artifact package of our study, the artifact material, and how to use the artifacts.
{"title":"Data and Materials for: Why Don’t Developers Detect Improper Input Validation?'; DROP TABLE Papers; --","authors":"Larissa Braz, Enrico Fregnan, G. Çalikli, Alberto Bacchelli","doi":"10.1109/ICSE-Companion52605.2021.00090","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00090","url":null,"abstract":"Improper Input Validation (IIV) is a dangerous software vulnerability that occurs when a system does not safely handle input data. Although IIV is easy to detect and fix, it still commonly happens in practice; so, why do developers not recognize IIV? Answering this question is key to understand how to support developers in creating secure software systems. In our work, we studied to what extent developers can detect IIV and investigate underlying reasons. To do so, we conducted an online experiment with 146 software developers. In this document, we explain how to obtain the artifact package of our study, the artifact material, and how to use the artifacts.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131211481","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00086
Gemma Catolino, Fabio Palomba, D. Tamburri, Alexander Serebrenik
In this document, we present the replication package of the paper "Understanding Community Smells Variability: A Statistical Approach" accepted at the 43rd International Conference on Software Engineering - Software Engineering in Society Track (ICSE '21).
{"title":"Understanding Community Smells Variability: A Statistical Approach: Replication Package Instructions","authors":"Gemma Catolino, Fabio Palomba, D. Tamburri, Alexander Serebrenik","doi":"10.1109/ICSE-Companion52605.2021.00086","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00086","url":null,"abstract":"In this document, we present the replication package of the paper \"Understanding Community Smells Variability: A Statistical Approach\" accepted at the 43rd International Conference on Software Engineering - Software Engineering in Society Track (ICSE '21).","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127216105","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00056
Reem Aleithan
Explaining the prediction results of software bug prediction models is a challenging task, which can provide useful information for developers to understand and fix the predicted bugs. Recently, Jirayus et al.'s proposed to use two model-agnostic techniques (i.e., LIME and iBreakDown) to explain the prediction results of bug prediction models. Although their experiments on file-level bug prediction show promising results, the performance of these techniques on explaining the results of just-in-time (i.e., change-level) bug prediction is unknown. This paper conducts the first empirical study to explore the explainability of these model-agnostic techniques on just-in-time bug prediction models. Specifically, this study takes a three-step approach, 1) replicating previously widely used just-in-time bug prediction models, 2) applying Local Interpretability Model-agnostic Explanation Technique (LIME) and iBreakDown on the prediction results, and 3) manually evaluating the explanations for buggy instances (i.e. positive predictions) against the root cause of the bugs. The results of our experiment show that LIME and iBreakDown fail to explain defect prediction explanations for just-in-time bug prediction models, unlike file-level. This paper urges for new approaches for explaining the results of just-in-time bug prediction models.
{"title":"Explainable Just-In-Time Bug Prediction: Are We There Yet?","authors":"Reem Aleithan","doi":"10.1109/ICSE-Companion52605.2021.00056","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00056","url":null,"abstract":"Explaining the prediction results of software bug prediction models is a challenging task, which can provide useful information for developers to understand and fix the predicted bugs. Recently, Jirayus et al.'s proposed to use two model-agnostic techniques (i.e., LIME and iBreakDown) to explain the prediction results of bug prediction models. Although their experiments on file-level bug prediction show promising results, the performance of these techniques on explaining the results of just-in-time (i.e., change-level) bug prediction is unknown. This paper conducts the first empirical study to explore the explainability of these model-agnostic techniques on just-in-time bug prediction models. Specifically, this study takes a three-step approach, 1) replicating previously widely used just-in-time bug prediction models, 2) applying Local Interpretability Model-agnostic Explanation Technique (LIME) and iBreakDown on the prediction results, and 3) manually evaluating the explanations for buggy instances (i.e. positive predictions) against the root cause of the bugs. The results of our experiment show that LIME and iBreakDown fail to explain defect prediction explanations for just-in-time bug prediction models, unlike file-level. This paper urges for new approaches for explaining the results of just-in-time bug prediction models.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131517434","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00139
Rashina Hoda
Grounded Theory, while becoming increasingly popular in software engineering, is also one of the most misunderstood, misused, and poorly presented and evaluated method in software engineering. When applied well, GT results in dense and valuable explanations of how and why phenomena occur in practice. GT can be applied as a full research method leading to mature theories and also in limited capacity for data analysis within other methods, using its robust open coding and constant comparison procedures. This technical briefing will go through the social origins of GT, present examples of grounded theories developed in SE, discuss the key challenges SE researchers face, and provide a gentle introduction to socio-technical grounded theory, a variant of GT for software engineering research.
{"title":"Decoding Grounded Theory for Software Engineering","authors":"Rashina Hoda","doi":"10.1109/ICSE-Companion52605.2021.00139","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00139","url":null,"abstract":"Grounded Theory, while becoming increasingly popular in software engineering, is also one of the most misunderstood, misused, and poorly presented and evaluated method in software engineering. When applied well, GT results in dense and valuable explanations of how and why phenomena occur in practice. GT can be applied as a full research method leading to mature theories and also in limited capacity for data analysis within other methods, using its robust open coding and constant comparison procedures. This technical briefing will go through the social origins of GT, present examples of grounded theories developed in SE, discuss the key challenges SE researchers face, and provide a gentle introduction to socio-technical grounded theory, a variant of GT for software engineering research.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132751237","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00107
Max Weber, S. Apel, Norbert Siegmund
These artifacts refer to the study and implementation of the paper 'White-Box Performance-Influence Models: A Profiling and Learning Approach'. In this document, we describe the idea and process of how to build white-box performance models for configurable software systems. Specifically, we describe the general steps and tools that we have used to implement our approach, the data we have obtained, and the evaluation setup. We further list the available artifacts, such as raw measurements, configurations, and scripts at our software heritage repository.
{"title":"White-Box Performance-Influence Models: A Profiling and Learning Approach (Replication Package)","authors":"Max Weber, S. Apel, Norbert Siegmund","doi":"10.1109/ICSE-Companion52605.2021.00107","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00107","url":null,"abstract":"These artifacts refer to the study and implementation of the paper 'White-Box Performance-Influence Models: A Profiling and Learning Approach'. In this document, we describe the idea and process of how to build white-box performance models for configurable software systems. Specifically, we describe the general steps and tools that we have used to implement our approach, the data we have obtained, and the evaluation setup. We further list the available artifacts, such as raw measurements, configurations, and scripts at our software heritage repository.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128215458","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00140
R. Torkar, Carlo A. Furia, R. Feldt
n/a
{"title":"Bayesian Data Analysis for Software Engineering","authors":"R. Torkar, Carlo A. Furia, R. Feldt","doi":"10.1109/ICSE-Companion52605.2021.00140","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00140","url":null,"abstract":"n/a","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128120413","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}
PLELog is a novel approach for log-based anomaly detection via probabilistic label estimation. It is designed to effectively detect anomalies in unlabeled logs and meanwhile avoid the manual labeling effort for training data generation. We use semantic information within log events as fixed-length vectors and apply HDBSCAN to automatically clustering log sequences. After that, we also propose a Probabilistic Label Estimation approach to reduce the noises introduced by error labeling and put "labeled" instances into an attention-based GRU network for training. We conducted an empirical study to evaluate the effectiveness of PLELog on two open-source log data (i.e., HDFS and BGL). The results demonstrate the effectiveness of PLELog. In particular, PLELog has been applied to two real-world systems from a university and a large corporation, further demonstrating its practicability.
{"title":"PLELog: Semi-Supervised Log-Based Anomaly Detection via Probabilistic Label Estimation","authors":"Lin Yang, Junjie Chen, Zan Wang, Weijing Wang, Jiajun Jiang, Xuyuan Dong, Wenbin Zhang","doi":"10.1109/ICSE-Companion52605.2021.00106","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00106","url":null,"abstract":"PLELog is a novel approach for log-based anomaly detection via probabilistic label estimation. It is designed to effectively detect anomalies in unlabeled logs and meanwhile avoid the manual labeling effort for training data generation. We use semantic information within log events as fixed-length vectors and apply HDBSCAN to automatically clustering log sequences. After that, we also propose a Probabilistic Label Estimation approach to reduce the noises introduced by error labeling and put \"labeled\" instances into an attention-based GRU network for training. We conducted an empirical study to evaluate the effectiveness of PLELog on two open-source log data (i.e., HDFS and BGL). The results demonstrate the effectiveness of PLELog. In particular, PLELog has been applied to two real-world systems from a university and a large corporation, further demonstrating its practicability.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124576041","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00081
A. Alshammari, Christopher Morris, Michael C Hilton, Jonathan Bell
This is an extended abstract that describes the code and data artifact [1] of our paper “FlakeFlagger: Predicting Flakiness Without Rerunning Tests” [2]. The goal of our artifact is to let researchers reproduce our experiment on our provided flaky dataset or reuse our tool on different flaky tests datasets.
{"title":"FlakeFlagger: Predicting Flakiness Without Rerunning Tests","authors":"A. Alshammari, Christopher Morris, Michael C Hilton, Jonathan Bell","doi":"10.1109/ICSE-Companion52605.2021.00081","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00081","url":null,"abstract":"This is an extended abstract that describes the code and data artifact [1] of our paper “FlakeFlagger: Predicting Flakiness Without Rerunning Tests” [2]. The goal of our artifact is to let researchers reproduce our experiment on our provided flaky dataset or reuse our tool on different flaky tests datasets.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122844282","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}
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