{"title":"Doctoral Symposium Program Committee","authors":"","doi":"10.1109/iceccs.2015.8","DOIUrl":"https://doi.org/10.1109/iceccs.2015.8","url":null,"abstract":"","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131517219","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 : 2022-05-01DOI: 10.1109/icse-companion52605.2021.00016
{"title":"Artifact Evaluation Program Committee","authors":"","doi":"10.1109/icse-companion52605.2021.00016","DOIUrl":"https://doi.org/10.1109/icse-companion52605.2021.00016","url":null,"abstract":"","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130949814","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}
Sheikh Iqbal Ahamed, U. Bellur, Neeran M. Karnik, M. Parashar
{"title":"Posters Program Committee","authors":"Sheikh Iqbal Ahamed, U. Bellur, Neeran M. Karnik, M. Parashar","doi":"10.1109/saso.2008.7","DOIUrl":"https://doi.org/10.1109/saso.2008.7","url":null,"abstract":"","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124130985","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-Companion52605.2021.00079
C. Menghi, Enrico Viganò, D. Bianculli, L. Briand
ThEodorE is a trace checker for Cyber-Physical systems (CPS). It provides users with (i) a GUI editor for writing CPS requirements; (ii) an automatic procedure to check whether the requirements hold on execution traces of a CPS. ThEodorE enables writing requirements using the Hybrid Logic of Signals (HLS), a novel, logic-based specification language to express CPS requirements. The trace checking procedure of ThEodorE reduces the problem of checking if a requirement holds on an execution trace to a satisfiability problem, which can be solved using off-the-shelf Satisfiability Modulo Theories (SMT) solvers. This artifact paper presents the tool support provided by ThEodorE.
{"title":"ThEodorE: a Trace Checker for CPS Properties","authors":"C. Menghi, Enrico Viganò, D. Bianculli, L. Briand","doi":"10.1109/ICSE-Companion52605.2021.00079","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00079","url":null,"abstract":"ThEodorE is a trace checker for Cyber-Physical systems (CPS). It provides users with (i) a GUI editor for writing CPS requirements; (ii) an automatic procedure to check whether the requirements hold on execution traces of a CPS. ThEodorE enables writing requirements using the Hybrid Logic of Signals (HLS), a novel, logic-based specification language to express CPS requirements. The trace checking procedure of ThEodorE reduces the problem of checking if a requirement holds on an execution trace to a satisfiability problem, which can be solved using off-the-shelf Satisfiability Modulo Theories (SMT) solvers. This artifact paper presents the tool support provided by ThEodorE.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586207","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-Companion52605.2021.00114
Xincheng He, Lei Xu, X. Zhang, Rui Hao, Yang Feng, Baowen Xu
This is the research artifact of the paper titled 'PyART: Python API Recommendation in Real-Time'. PyART is a real-time API recommendation tool for Python, which includes two main functions: data-flow analysis and real-time API recommendation for both incomplete and complete Python code context. Compared to classical tools, PyART has two important particularities: it is able to work on real-time recommendation scenario, and it provides data-flow analysis and API recommendation for dynamic language. Classical tools often fail to make static analysis in real-time recommendation scenario, due to the incompletion of syntax. And the dynamic features of Python language also bring challenges to type inference and API recommendation. Different from classical tools, PyART derives optimistic data-flow that is neither sound nor complete but sufficient for API recommendation and cost-effective to collect, and provides real-time API recommendations based on novel candidate collection, context analysis and feature learning techniques. The artifact evaluation experiments of PyART include three main aspects: data-flow analysis, intra-project API recommendation and across-project API recommendation. We assume users of the artifact is able to use Linux Ubuntu Operating System.
{"title":"PyART: Python API Recommendation in Real-Time","authors":"Xincheng He, Lei Xu, X. Zhang, Rui Hao, Yang Feng, Baowen Xu","doi":"10.1109/ICSE-Companion52605.2021.00114","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00114","url":null,"abstract":"This is the research artifact of the paper titled 'PyART: Python API Recommendation in Real-Time'. PyART is a real-time API recommendation tool for Python, which includes two main functions: data-flow analysis and real-time API recommendation for both incomplete and complete Python code context. Compared to classical tools, PyART has two important particularities: it is able to work on real-time recommendation scenario, and it provides data-flow analysis and API recommendation for dynamic language. Classical tools often fail to make static analysis in real-time recommendation scenario, due to the incompletion of syntax. And the dynamic features of Python language also bring challenges to type inference and API recommendation. Different from classical tools, PyART derives optimistic data-flow that is neither sound nor complete but sufficient for API recommendation and cost-effective to collect, and provides real-time API recommendations based on novel candidate collection, context analysis and feature learning techniques. The artifact evaluation experiments of PyART include three main aspects: data-flow analysis, intra-project API recommendation and across-project API recommendation. We assume users of the artifact is able to use Linux Ubuntu Operating System.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126972872","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-Companion52605.2021.00097
S. Maoz, Rafi Shalom
This document describes the artifact that accompanies the ICSE'21 paper "Unrealizable Cores for Reactive Systems Specifications". The artifact includes the specifications that were used in the experiments that are described in the paper. It further includes an executable that allows interested readers to reproduce these experiments and inspect their results. Additionally, the executable is applicable to any specification in Spectra format, which allows conducting similar experiments over any Spectra specification. We hope the artifact will be useful for researchers who are interested in reactive synthesis, specifically in different means to deal with unrealizable specifications.
{"title":"Unrealizable Cores for Reactive Systems Specifications: Artifact","authors":"S. Maoz, Rafi Shalom","doi":"10.1109/ICSE-Companion52605.2021.00097","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00097","url":null,"abstract":"This document describes the artifact that accompanies the ICSE'21 paper \"Unrealizable Cores for Reactive Systems Specifications\". The artifact includes the specifications that were used in the experiments that are described in the paper. It further includes an executable that allows interested readers to reproduce these experiments and inspect their results. Additionally, the executable is applicable to any specification in Spectra format, which allows conducting similar experiments over any Spectra specification. We hope the artifact will be useful for researchers who are interested in reactive synthesis, specifically in different means to deal with unrealizable specifications.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122363860","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-Companion52605.2021.00028
Gabriele Beßler, Joshimar Cordova, Shaheen Cullen-Baratloo, Sofiane Dissem, Emily Lu, Sofia Devin, Ibrahim Abughararh, Lucas Bang
This paper presents Metrinome, a tool for performing automatic path complexity analysis of C functions. The path complexity of a function is an expression that describes the number of paths through the function up to a given execution depth. Metrinome constructs the control flow graph CFG of a C function using LLVM utilities, analyzes that CFG using algebraic graph theory and analytic combinatorics, and produces a closed-form expression for the path complexity as well as the asymptotic path complexity of the function. Our experiments show that path complexity predicts the growth rate of the number of execution paths that Klee, a popular symbolic execution tool, is able to cover within a given exploration depth. Metrinome is open-source, available as a Docker image for immediate use, and all of our experiments and data are available in our repository and included in our Docker image.
{"title":"Metrinome: Path Complexity Predicts Symbolic Execution Path Explosion","authors":"Gabriele Beßler, Joshimar Cordova, Shaheen Cullen-Baratloo, Sofiane Dissem, Emily Lu, Sofia Devin, Ibrahim Abughararh, Lucas Bang","doi":"10.1109/ICSE-Companion52605.2021.00028","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00028","url":null,"abstract":"This paper presents Metrinome, a tool for performing automatic path complexity analysis of C functions. The path complexity of a function is an expression that describes the number of paths through the function up to a given execution depth. Metrinome constructs the control flow graph CFG of a C function using LLVM utilities, analyzes that CFG using algebraic graph theory and analytic combinatorics, and produces a closed-form expression for the path complexity as well as the asymptotic path complexity of the function. Our experiments show that path complexity predicts the growth rate of the number of execution paths that Klee, a popular symbolic execution tool, is able to cover within a given exploration depth. Metrinome is open-source, available as a Docker image for immediate use, and all of our experiments and data are available in our repository and included in our Docker image.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114186952","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-Companion52605.2021.00092
Jordan Samhi, Alexandre Bartel, Tegawendé F. Bissyandé, Jacques Klein
This short paper presents the artefacts related to our ICSE 2021 research paper.
这篇短文介绍了与我们的ICSE 2021研究论文相关的人工制品。
{"title":"A Partial Replication of \"RAICC: Revealing Atypical Inter-Component Communication in Android Apps\"","authors":"Jordan Samhi, Alexandre Bartel, Tegawendé F. Bissyandé, Jacques Klein","doi":"10.1109/ICSE-Companion52605.2021.00092","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00092","url":null,"abstract":"This short paper presents the artefacts related to our ICSE 2021 research paper.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114577213","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-Companion52605.2021.00088
Vitalis Salis, Thodoris Sotiropoulos, P. Louridas, D. Spinellis, Dimitris Mitropoulos
The ICSE 2021 paper titled “PyCG: Practical Call Graph Generation in Python” comes with a replication package with the purpose of providing open access to (1) our prototype call graph generator, namely PyCG, and (2) the data and scripts that replicate the results of the paper. The Artifact Evaluation Committee found that this package leads to the reproduction of the results outlined in the paper and is openly available1. The replication package contains the following: 1) A Docker image which can be either built manually or downloaded from DockerHub. It contains the source code and installation of PyCG, as well as the installations of two other call graph generators (i.e., Pyan and Depends), which we compare PyCG with. 2) A micro-benchmark suite of 112 Python modules (Section I-A). 3) A macro-benchmark suite of 5 popular Python packages (Section I-B). 4) Python and Bash scripts used to execute PyCG, Pyan and Depends against the micro- and macro-benchmarks and compare the corresponding results.
{"title":"A Replication Package for PyCG: Practical Call Graph Generation in Python","authors":"Vitalis Salis, Thodoris Sotiropoulos, P. Louridas, D. Spinellis, Dimitris Mitropoulos","doi":"10.1109/ICSE-Companion52605.2021.00088","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00088","url":null,"abstract":"The ICSE 2021 paper titled “PyCG: Practical Call Graph Generation in Python” comes with a replication package with the purpose of providing open access to (1) our prototype call graph generator, namely PyCG, and (2) the data and scripts that replicate the results of the paper. The Artifact Evaluation Committee found that this package leads to the reproduction of the results outlined in the paper and is openly available1. The replication package contains the following: 1) A Docker image which can be either built manually or downloaded from DockerHub. It contains the source code and installation of PyCG, as well as the installations of two other call graph generators (i.e., Pyan and Depends), which we compare PyCG with. 2) A micro-benchmark suite of 112 Python modules (Section I-A). 3) A macro-benchmark suite of 5 popular Python packages (Section I-B). 4) Python and Bash scripts used to execute PyCG, Pyan and Depends against the micro- and macro-benchmarks and compare the corresponding results.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114729043","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-Companion52605.2021.00038
Sarah Fakhoury, Devjeet Roy, Harry Pines, Tyler Cleveland, Cole S. Peterson, V. Arnaoudova, Bonita Sharif, Jonathan I. Maletic
Eye tracking tools are used in software engineering research to study various software development activities. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace [9] community infrastructure. We introduce the iTrace-Atom plugin and gazel [g@"zel]—a Python data processing pipeline that maps gaze information to changing source code elements and provides researchers with a way to query this dynamic data. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. iTrace and gazel completely revolutionize the way eye tracking studies are conducted in realistic settings with the presence of scrolling, context switching, and now editing. This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring.
{"title":"gazel: Supporting Source Code Edits in Eye-Tracking Studies","authors":"Sarah Fakhoury, Devjeet Roy, Harry Pines, Tyler Cleveland, Cole S. Peterson, V. Arnaoudova, Bonita Sharif, Jonathan I. Maletic","doi":"10.1109/ICSE-Companion52605.2021.00038","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00038","url":null,"abstract":"Eye tracking tools are used in software engineering research to study various software development activities. However, a major limitation of these tools is their inability to track gaze data for activities that involve source code editing. We present a novel solution to support eye tracking experiments for tasks involving source code edits as an extension of the iTrace [9] community infrastructure. We introduce the iTrace-Atom plugin and gazel [g@\"zel]—a Python data processing pipeline that maps gaze information to changing source code elements and provides researchers with a way to query this dynamic data. iTrace-Atom is evaluated via a series of simulations and is over 99% accurate at high eye-tracking speeds of over 1,000Hz. iTrace and gazel completely revolutionize the way eye tracking studies are conducted in realistic settings with the presence of scrolling, context switching, and now editing. This opens the doors to support many day-to-day software engineering tasks such as bug fixing, adding new features, and refactoring.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129524864","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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