Pub Date : 2019-09-01DOI: 10.1109/VISSOFT.2019.00011
Yuya Tomida, Yoshiki Higo, S. Matsumoto, S. Kusumoto
Automated program repair (in short, APR) techniques that utilize genetic algorithm (in short, GA) have a capability of repairing programs even if the programs require multiple code fragments to be changed. Those techniques repeat program generation, program evaluation, and program selection until a generated program passes all given test cases. Those techniques occasionally generate a large number of programs before a repaired program is generated. Thus, it is difficult to understand how an input program is evolved in the loop processing of genetic algorithm. In this paper, we are inspired by genealogy and propose a new technique to visualize program evolution in the process of automated program repair. We have implemented the proposed technique as a software tool for kGenProg, which is one of GA-based APR tools. We evaluated the proposed technique with the developers of kGenProg. In the evaluation, the developers found latent issues in kGenProg's processing and came up with new ideas to improve program generation. From those results, we conclude that our visualization is useful to understand program evolution in the APR process.
{"title":"Visualizing Code Genealogy: How Code is Evolutionarily Fixed in Program Repair?","authors":"Yuya Tomida, Yoshiki Higo, S. Matsumoto, S. Kusumoto","doi":"10.1109/VISSOFT.2019.00011","DOIUrl":"https://doi.org/10.1109/VISSOFT.2019.00011","url":null,"abstract":"Automated program repair (in short, APR) techniques that utilize genetic algorithm (in short, GA) have a capability of repairing programs even if the programs require multiple code fragments to be changed. Those techniques repeat program generation, program evaluation, and program selection until a generated program passes all given test cases. Those techniques occasionally generate a large number of programs before a repaired program is generated. Thus, it is difficult to understand how an input program is evolved in the loop processing of genetic algorithm. In this paper, we are inspired by genealogy and propose a new technique to visualize program evolution in the process of automated program repair. We have implemented the proposed technique as a software tool for kGenProg, which is one of GA-based APR tools. We evaluated the proposed technique with the developers of kGenProg. In the evaluation, the developers found latent issues in kGenProg's processing and came up with new ideas to improve program generation. From those results, we conclude that our visualization is useful to understand program evolution in the APR process.","PeriodicalId":375862,"journal":{"name":"2019 Working Conference on Software Visualization (VISSOFT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132061409","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 : 2019-09-01DOI: 10.1109/VISSOFT.2019.00009
Juan Pablo Sandoval Alcocer, Fabian Beck, Alexandre Bergel
Software performance may be significantly affected by source code modifications. Understanding the effect of these changes along different software versions is a challenging and necessary activity to debug performance failures. It is not sufficiently supported by existing profiling tools and visualization approaches. Practitioners would need to manually compare calling context trees and call graphs. We aim at better supporting the comparison of benchmark executions along multiple software versions. We propose Performance Evolution Matrix, an interactive visualization technique that contrasts runtime metrics to source code changes. It combines a comparison of time series data and execution graphs in a matrix layout, showing performance and source code metrics at different levels of granularity. The approach guides practitioners from the high-level identification of a performance regression to the changes that might have caused the issue. We conducted a controlled experiment with 12 participants to provide empirical evidence of the viability of our method. The results indicate that our approach can reduce the effort for identifying sources of performance regressions compared to traditional profiling visualizations.
{"title":"Performance Evolution Matrix: Visualizing Performance Variations Along Software Versions","authors":"Juan Pablo Sandoval Alcocer, Fabian Beck, Alexandre Bergel","doi":"10.1109/VISSOFT.2019.00009","DOIUrl":"https://doi.org/10.1109/VISSOFT.2019.00009","url":null,"abstract":"Software performance may be significantly affected by source code modifications. Understanding the effect of these changes along different software versions is a challenging and necessary activity to debug performance failures. It is not sufficiently supported by existing profiling tools and visualization approaches. Practitioners would need to manually compare calling context trees and call graphs. We aim at better supporting the comparison of benchmark executions along multiple software versions. We propose Performance Evolution Matrix, an interactive visualization technique that contrasts runtime metrics to source code changes. It combines a comparison of time series data and execution graphs in a matrix layout, showing performance and source code metrics at different levels of granularity. The approach guides practitioners from the high-level identification of a performance regression to the changes that might have caused the issue. We conducted a controlled experiment with 12 participants to provide empirical evidence of the viability of our method. The results indicate that our approach can reduce the effort for identifying sources of performance regressions compared to traditional profiling visualizations.","PeriodicalId":375862,"journal":{"name":"2019 Working Conference on Software Visualization (VISSOFT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122151265","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 : 2019-08-12DOI: 10.1109/VISSOFT.2019.00014
Leonel Merino, E. Kozlova, Oscar Nierstrasz, D. Weiskopf
Although many tools have been presented in the research literature of software visualization, there is little evidence of their adoption. To choose a suitable visualization tool, practitioners need to analyze various characteristics of tools such as their supported software concerns and level of maturity. Indeed, some tools can be prototypes for which the lifespan is expected to be short, whereas others can be fairly mature products that are maintained for a longer time. Although such characteristics are often described in papers, we conjecture that practitioners willing to adopt software visualizations require additional support to discover suitable visualization tools. In this paper, we elaborate on our efforts to provide such support. To this end, we systematically analyzed research papers in the literature of software visualization and curated a catalog of 70 available tools that employ various visualization techniques to support the analysis of multiple software concerns. We further encapsulate these characteristics in an ontology. VISON, our software visualization ontology, captures these semantics as concepts and relationships. We report on early results of usage scenarios that demonstrate how the ontology can support (i) developers to find suitable tools for particular development concerns, and (ii) researchers who propose new software visualization tools to identify a baseline tool for a controlled experiment.
{"title":"VISON: An Ontology-Based Approach for Software Visualization Tool Discoverability","authors":"Leonel Merino, E. Kozlova, Oscar Nierstrasz, D. Weiskopf","doi":"10.1109/VISSOFT.2019.00014","DOIUrl":"https://doi.org/10.1109/VISSOFT.2019.00014","url":null,"abstract":"Although many tools have been presented in the research literature of software visualization, there is little evidence of their adoption. To choose a suitable visualization tool, practitioners need to analyze various characteristics of tools such as their supported software concerns and level of maturity. Indeed, some tools can be prototypes for which the lifespan is expected to be short, whereas others can be fairly mature products that are maintained for a longer time. Although such characteristics are often described in papers, we conjecture that practitioners willing to adopt software visualizations require additional support to discover suitable visualization tools. In this paper, we elaborate on our efforts to provide such support. To this end, we systematically analyzed research papers in the literature of software visualization and curated a catalog of 70 available tools that employ various visualization techniques to support the analysis of multiple software concerns. We further encapsulate these characteristics in an ontology. VISON, our software visualization ontology, captures these semantics as concepts and relationships. We report on early results of usage scenarios that demonstrate how the ontology can support (i) developers to find suitable tools for particular development concerns, and (ii) researchers who propose new software visualization tools to identify a baseline tool for a controlled experiment.","PeriodicalId":375862,"journal":{"name":"2019 Working Conference on Software Visualization (VISSOFT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114418569","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 : 2019-04-01DOI: 10.1109/pacificvis.2019.00005
Ross Maciejewski, Jinwook Seo, Rüdiger Westermann, Hsiang-Yun Wu, Fabian Beck
{"title":"Welcome from the Chairs","authors":"Ross Maciejewski, Jinwook Seo, Rüdiger Westermann, Hsiang-Yun Wu, Fabian Beck","doi":"10.1109/pacificvis.2019.00005","DOIUrl":"https://doi.org/10.1109/pacificvis.2019.00005","url":null,"abstract":"","PeriodicalId":375862,"journal":{"name":"2019 Working Conference on Software Visualization (VISSOFT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126295218","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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