Article

Visualization-based analysis of quality for large-scale software systems

Authors:

Guillaume Langelier,

Houari Sahraoui,

Pierre PoulinAuthors Info & Claims

ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering

Pages 214 - 223

https://doi.org/10.1145/1101908.1101941

Published: 07 November 2005 Publication History

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Abstract

We propose an approach for complex software analysis based on visualization. Our work is motivated by the fact that in spite of years of research and practice, software development and maintenance are still time and resource consuming, and high-risk activities. The most important reason in our opinion is the complexity of many phenomena related to software, such as its evolution and its reliability. In fact, there is very little theory explaining them. Today, we have a unique opportunity to empirically study these phenomena, thanks to large sets of software data available through open-source programs and open repositories. Automatic analysis techniques, such as statistics and machine learning, are usually limited when studying phenomena with unknown or poorly-understood influence factors. We claim that hybrid techniques that combine automatic analysis with human expertise through visualization are excellent alternatives to them. In this paper, we propose a visualization framework that supports quality analysis of large-scale software systems. We circumvent the problem of size by exploiting perception capabilities of the human visual system.

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Cited By

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Agouf NLabsari SDucasse SEtien AAnquetil N(2023)A Visualization for Client-Server Architecture Assessement2023 IEEE Working Conference on Software Visualization (VISSOFT)10.1109/VISSOFT60811.2023.00010(1-11)Online publication date: 1-Oct-2023
https://doi.org/10.1109/VISSOFT60811.2023.00010
Occhipinti GNagy CMinelli RLanza M(2023)SYN: Ultra-Scale Software Evolution Comprehension2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)10.1109/ICPC58990.2023.00020(69-73)Online publication date: May-2023
https://doi.org/10.1109/ICPC58990.2023.00020
Kaur SSingh S(2023)Object Oriented Metrics Based Empirical Model for Predicting “Code Smells” in Open Source SoftwareJournal of The Institution of Engineers (India): Series B10.1007/s40031-022-00833-4104:1(241-257)Online publication date: 3-Jan-2023
https://doi.org/10.1007/s40031-022-00833-4
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Index Terms

Visualization-based analysis of quality for large-scale software systems
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
2. Software and its engineering

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Reviews

Reviewer: Ponmurugarajan Thiyagarajan

Analyzing complex software is not an easy task. In many instances, software engineers have to depend on automated techniques to perform this task. This paper highlights the human ability to perform complex software analysis, and discusses a hybrid technique that has human involvement and is semi-automated. The power of human visualization capabilities is utilized by the technique, which the authors propose for analysis tasks. The authors describe a visualization framework that has four aspects: class representation, program representation, navigation, and data filtering. In this framework, software code is represented as some arbitrary figure. Software metrics like cohesion, coupling between objects, and so on are used to link a class with a representation. Two layout techniques, Treemap and Sunburst, are also used in the experiment detailed at the end of the paper. A camera model is used for navigation, and data filters are used to focus on a subset of elements. An experiment has been conducted to test the proposed framework. The results are convincing, and show that less time is taken to perform complex software analysis tasks on small-to-medium size programs. Another interesting inference of the experiment was that sophisticated layout techniques play an important role, and Treemap seemed to perform better than Sunburst in a few cases. The authors admit that the proposed framework has limitations, but future researchers can work on them. To conclude, this paper describes nice, innovative work, and explains many new concepts. Online Computing Reviews Service

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Information & Contributors

Information

Published In

ASE '05: Proceedings of the 20th IEEE/ACM International Conference on Automated Software Engineering

November 2005

482 pages

ISBN:1581139934

DOI:10.1145/1101908

General Chair:
David Redmiles
University of California, Irvine, CA
,
Program Chairs:
Tom Ellman
Vassar College
,
Andrea Zisman
City University, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2005

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ASE05

Sponsor:

ASE05: International Conference on Automated Software Engineering 2005

November 7 - 11, 2005

CA, Long Beach, USA

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Overall Acceptance Rate 82 of 337 submissions, 24%

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Cited By

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Agouf NLabsari SDucasse SEtien AAnquetil N(2023)A Visualization for Client-Server Architecture Assessement2023 IEEE Working Conference on Software Visualization (VISSOFT)10.1109/VISSOFT60811.2023.00010(1-11)Online publication date: 1-Oct-2023
https://doi.org/10.1109/VISSOFT60811.2023.00010
Occhipinti GNagy CMinelli RLanza M(2023)SYN: Ultra-Scale Software Evolution Comprehension2023 IEEE/ACM 31st International Conference on Program Comprehension (ICPC)10.1109/ICPC58990.2023.00020(69-73)Online publication date: May-2023
https://doi.org/10.1109/ICPC58990.2023.00020
Kaur SSingh S(2023)Object Oriented Metrics Based Empirical Model for Predicting “Code Smells” in Open Source SoftwareJournal of The Institution of Engineers (India): Series B10.1007/s40031-022-00833-4104:1(241-257)Online publication date: 3-Jan-2023
https://doi.org/10.1007/s40031-022-00833-4
Nagy CLanza MCleve A(2023)Mining, Analyzing, and Evolving Data-Intensive Software EcosystemsSoftware Ecosystems10.1007/978-3-031-36060-2_11(281-314)Online publication date: 6-Oct-2023
https://doi.org/10.1007/978-3-031-36060-2_11
Kaur SSingh S(2023)Improving the Quality of Open Source SoftwareAgile Software Development10.1002/9781119896838.ch16(309-323)Online publication date: 8-Feb-2023
https://doi.org/10.1002/9781119896838.ch16
Nguyen Thanh BNguyen N. H. MLe Thi My HNguyen Thanh B(2022)ml-Codesmell: A code smell prediction dataset for machine learning approachesProceedings of the 11th International Symposium on Information and Communication Technology10.1145/3568562.3568643(368-374)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1145/3568562.3568643
Ardigò SNagy CMinelli RLanza MDwyer M(2022)M3triCityProceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings10.1145/3510454.3516831(130-133)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510454.3516831
Tang LWu TChen XWen SLi LXia XGrobler MXiang Y(2022)How does Visualisation Help App Practitioners Analyse Android Apps?IEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.3178181(1-1)Online publication date: 2022
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Ardigo SNagy CMinelli RLanza M(2022)M3triCity: Visualizing Evolving Software & Data Cities2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)10.1109/ICSE-Companion55297.2022.9793749(130-133)Online publication date: May-2022
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Limberger DScheibel WDöllner JTrapp M(2022)Visual variables and configuration of software mapsJournal of Visualization10.1007/s12650-022-00868-126:1(249-274)Online publication date: 16-Sep-2022
https://doi.org/10.1007/s12650-022-00868-1
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