Ownership at Large -- Open Problems and Challenges in Ownership Management

Ahlgren, John; Berezin, Maria Eugenia; Bojarczuk, Kinga; Dulskyte, Elena; Dvortsova, Inna; George, Johann; Gucevska, Natalija; Harman, Mark; He, Shan; Lämmel, Ralf; Meijer, Erik; Sapora, Silvia; Spahr-Summers, Justin

doi:10.48550/arxiv.2004.07352

Cited by 1 publication

(2 citation statements)

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“…For example, Facebook currently has an Ownesty system that aims to identify who is the most suitable owner of a given asset changes over time, i.e. code ownership [1]. Prior studies show that code ownership metrics share a strong relationship with defect-proneness [3].…”

Section: Lessons Learned and Open Questionsmentioning

confidence: 99%

“…In this paper, we conducted a qualitative survey to understand the practitioners' perceptions of the goals of defect prediction models and the model-agnostic techniques for generating visual explanations of defect prediction models. The analysis of related work led us to focus on three goals of defect prediction models: (1) prioritizing the limited SQA resources on the most risky files; (2) understanding the characteristics that are associated with software defects in the past; and (3) understanding the most important characteristics that contributed to a prediction of a file. We asked respondents to assess the perceived usefulness and their willingness to adopt defect prediction models.…”

Section: Introductionmentioning

confidence: 99%

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Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models

Jiarpakdee¹,

Tantithamthavorn²,

Grundy³

2021

Preprint

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Software defect prediction models are classifiers that are constructed from historical software data. Such software defect prediction models have been proposed to help developers optimize the limited Software Quality Assurance (SQA) resources and help managers develop SQA plans. Prior studies have different goals for their defect prediction models and use different techniques for generating visual explanations of their models. Yet, it is unclear what are the practitioners' perceptions of (1) these defect prediction model goals, and (2) the model-agnostic techniques used to visualize these models. We conducted a qualitative survey to investigate practitioners' perceptions of the goals of defect prediction models and the model-agnostic techniques used to generate visual explanations of defect prediction models. We found that (1) 82%-84% of the respondents perceived that the three goals of defect prediction models are useful; (2) LIME is the most preferred technique for understanding the most important characteristics that contributed to a prediction of a file, while ANOVA/VarImp is the second most preferred technique for understanding the characteristics that are associated with software defects in the past. Our findings highlight the significance of investigating how to improve the understanding of defect prediction models and their predictions. Hence, model-agnostic techniques from explainable AI domain may help practitioners to understand defect prediction models and their predictions.

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Section: Lessons Learned and Open Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%