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Smart Measurements and Analysis for Software Quality Enhancement

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Software Technologies (ICSOFT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1077))

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Abstract

Requests to improve the quality of software are increasing due to the competition in software industry and the complexity of software development integrating multiple technology domains (e.g., IoT, Big Data, Cloud, Artificial Intelligence, Security Technologies). Measurements collection and analysis is key activity to assess software quality during its development live-cycle. To optimize this activity, our main idea is to periodically select relevant measures to be executed (among a set of possible measures) and automatize their analysis by using a dedicated tool. The proposed solution is integrated in a whole PaaS platform called MEASURE. The tools supporting this activity are Software Metric Suggester tool that recommends metrics of interest according several software development constraints and based on artificial intelligence and MINT tool that correlates collected measurements and provides near real-time recommendations to software development stakeholders (i.e. DevOps team, project manager, human resources manager etc.) to improve the quality of the development process. To illustrate the efficiency of both tools, we created different scenarios on which both approaches are applied. Results show that both tools are complementary and can be used to improve the software development process and thus the final software quality.

Supported by the ongoing European project ITEA3-MEASURE started in Dec. 1st, 2015, and the EU HubLinked project started in Jan. 1st, 2017.

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Notes

  1. 1.

    http://www.statsoft.com/Textbook/Support-Vector-Machines.

  2. 2.

    https://itea3.org/project/measure.html.

  3. 3.

    http://www.hublinked.eu/.

  4. 4.

    https://github.com/ITEA3-Measure/.

  5. 5.

    https://www.omg.org/spec/SMM/About-SMM/.

  6. 6.

    https://www.modelio.org/.

  7. 7.

    http://www.montimage.com/products.html.

  8. 8.

    http://194.2.241.244/measure/.

  9. 9.

    https://github.com/annoviko/pyclustering.

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Acknowledgment

This work is partially funded by the ongoing European project ITEA3-MEASURE started in Dec. 1st, 2015, and the EU HubLinked project started in Jan. 1st, 2017.

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Authors and Affiliations

  1. SAMOVAR, Telecom SudParis, Université Paris-Saclay, Saint-Aubin, France

    Sarah Dahab, Stephane Maag & Ana Cavalli

  2. Montimage Research and Development, Paris, France

    Wissam Mallouli

Authors
  1. Sarah Dahab
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  2. Stephane Maag
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  3. Wissam Mallouli
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  4. Ana Cavalli
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Corresponding author

Correspondence to Wissam Mallouli .

Editor information

Editors and Affiliations

  1. Information Systems Group, University of Twente, Enschede, The Netherlands

    Marten van Sinderen

  2. Wrocław University of Economics, Wrocław, Poland

    Leszek A. Maciaszek

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Dahab, S., Maag, S., Mallouli, W., Cavalli, A. (2019). Smart Measurements and Analysis for Software Quality Enhancement. In: van Sinderen, M., Maciaszek, L. (eds) Software Technologies. ICSOFT 2018. Communications in Computer and Information Science, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-030-29157-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-29157-0_9

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