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Monitoring Software Quality by Means of Simulation Methods

Published: 08 September 2016 Publication History

Abstract

The evolution of software projects is driven by developers who are in control of the developed artifacts and the quality of software projects depends on the work of participating developers. Thus, a simulation tool requires a suitable model of the commit behavior of different developer types. In this paper, we present an agent-based model for software processes containing the commit behavior for different developer types. The description of these types results from mining software repositories. Since relationships between software entities, e.g., files, classes, modules, axe represented as dependency graphs, simulation results can be assessed automatically by Conditional Random Fields (CRFs). By adjusting simulation parameters for one project we are able to give a quality trend of other projects similar in size and duration only by changing the effort and the size of other projects to simulate.

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Cited By

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  • (2021)Using system dynamics to teach about dependencies, correlation and systemic thinking on the software process workflowsIET Software10.1049/sfw2.1203115:6(351-364)Online publication date: 23-Jun-2021
  • (2021)Investigation and prediction of open source software evolution using automated parameter mining for agent-based simulationAutomated Software Engineering10.1007/s10515-021-00280-328:1Online publication date: 14-May-2021
  • (2020)Software Process Simulation ModelingComputer Standards & Interfaces10.1016/j.csi.2020.10342570:COnline publication date: 1-Jun-2020
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cover image ACM Conferences
ESEM '16: Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement
September 2016
457 pages
ISBN:9781450344272
DOI:10.1145/2961111
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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Publication History

Published: 08 September 2016

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Author Tags

  1. Agent-Based Simulation
  2. Conditional Random Fields
  3. Mining Software Repositories
  4. Software Dependency Analysis

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ESEM '16 Paper Acceptance Rate 27 of 122 submissions, 22%;
Overall Acceptance Rate 130 of 594 submissions, 22%

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Cited By

View all
  • (2021)Using system dynamics to teach about dependencies, correlation and systemic thinking on the software process workflowsIET Software10.1049/sfw2.1203115:6(351-364)Online publication date: 23-Jun-2021
  • (2021)Investigation and prediction of open source software evolution using automated parameter mining for agent-based simulationAutomated Software Engineering10.1007/s10515-021-00280-328:1Online publication date: 14-May-2021
  • (2020)Software Process Simulation ModelingComputer Standards & Interfaces10.1016/j.csi.2020.10342570:COnline publication date: 1-Jun-2020
  • (2018)Assessing Simulated Software Graphs Using Conditional Random FieldsSimulation Science10.1007/978-3-319-96271-9_15(239-250)Online publication date: 8-Aug-2018
  • (2018)Simulating Software Refactorings Based on Graph TransformationsSimulation Science10.1007/978-3-319-96271-9_10(161-175)Online publication date: 8-Aug-2018
  • (2017)Agent-Based Simulation for Software Development ProcessesMulti-Agent Systems and Agreement Technologies10.1007/978-3-319-59294-7_28(333-340)Online publication date: 23-Jun-2017

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