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Desnos, Nicolas; Huchard, Marianne; Tremblay, Guy; Urtado, Christelle; Vauttier, Sylvain

doi:10.1002/smr.v20:5

Cited by 23 publications

(1 citation statement)

References 85 publications

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“…In that approach, we look through the developer's own history to find old examples where they have made the kinds of changes recommended by the plan. Other researchers also adopt this approach (see (Nayrolles and Hamou-Lhadj, 2018) at MSR 2018). In the following:…”

Section: Choice Of Software Metricsmentioning

confidence: 99%

Learning Actionable Analytics from Multiple Software Projects

Krishna¹

2017

Preprint

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The current generation of software analytics tools are mostly prediction algorithms (e.g. support vector machines, naive bayes, logistic regression, etc). While prediction is useful, after prediction comes planning about what actions to take in order to improve quality. This research seeks methods that generate demonstrably useful guidance on what to do within the context of a specific software project. Specifically, we propose XTREE (for within-project planning) and BELLTREE (for cross-project planning) to generating plans that can improve software quality. Each such plan has the property that, if followed, it reduces the expected number of future defect reports. To find this expected number, planning was first applied to data from release x. Next, we looked for change in release x + 1 that conformed to our plans. This procedure was applied using a range of planners from the literature, as well as XTREE. In 10 open-source JAVA systems, several hundreds of defects were reduced in sections of the code that conformed to XTREE's plans. Further, when compared to other planners, XTREE's plans were found to be easier to implement (since they were shorter) and more effective at reducing the expected number of defects.

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Section: Choice Of Software Metricsmentioning

confidence: 99%

Learning Actionable Analytics from Multiple Software Projects

Krishna¹

2017

Preprint

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An empirical investigation of single‐objective and multiobjective evolutionary algorithms for developer's assignment to bugs

Karim

Ruhe

Rahman³

et al. 2016

J Software Evolu Process

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In this paper, the modeling of developers' assignment to bugs (DAB) is studied. The problem is modeled both as a single objective (minimize bug fix time) and as a bi-objective (minimize bug fix time and cost) combinatorial optimization problem. Two models of developer assignment are considered where in the first model a single developer is assigned per bug (single developer model), while in the second model a single developer is assigned for each competency area of a bug (individual competency model). The latter model is proposed in this paper. For the single developer model, GA@DAB, an existing genetic algorithm-based approach, is extended to support precedence among bugs. For the individual competency model of DAB, one genetic algorithm-based approach (Competence@DAB) and one nondominated sorting genetic algorithm II-based approach (CompetenceMulti 2 @DAB ) are proposed to generate solutions minimizing time and minimizing both time and cost, respectively. The performance of the proposed approaches was evaluated for 2040 bugs of 19 open-source milestone projects from the ECLIPSE platform. Our results and analysis show that the proposed individual competency model is far better than the single developer model, with average bug fix time reduction of 39.7% across all projects.

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Learning actionable analytics from multiple software projects

Krishna

2020

Empir Software Eng

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Untitled

Cited by 23 publications

References 85 publications

Learning Actionable Analytics from Multiple Software Projects

Learning Actionable Analytics from Multiple Software Projects

An empirical investigation of single‐objective and multiobjective evolutionary algorithms for developer's assignment to bugs

Learning actionable analytics from multiple software projects

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