Using dynamic and contextual features to predict issue lifetime in GitHub projects

Kikas, Riivo; Dumas, Marlon; Pfahl, Dietmar

doi:10.1145/2901739.2901751

Cited by 59 publications

(49 citation statements)

References 24 publications

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“…However, there are also studies that rely on different metrics and methodologies to select GitHub projects. issues and commits to obtain a large set of GitHub projects and study models to predict whether an issue will be closed [44].…”

Section: Related Workmentioning

confidence: 99%

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

Borges¹,

Valente²

2018

Journal of Systems and Software

231

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Besides a git-based version control system, GitHub integrates several social coding features. Particularly, GitHub users can star a repository, presumably to manifest interest or satisfaction with an open source project. However, the real and practical meaning of starring a project was never the subject of an in-depth and well-founded empirical investigation. Therefore, we provide in this paper a throughout study on the meaning, characteristics, and dynamic growth of GitHub stars. First, by surveying 791 developers, we report that three out of four developers consider the number of stars before using or contributing to a GitHub project. Then, we report a quantitative analysis on the characteristics of the top-5,000 most starred GitHub repositories. We propose four patterns to describe stars growth, which are derived after clustering the time series representing the number of stars of the studied repositories; we also reveal the perception of 115 developers about these growth patterns. To conclude, we provide a list of recommendations to open source project managers (e.g., on the importance of social media promotion) and to GitHub users and Software Engineering researchers (e.g., on the risks faced when selecting projects by GitHub stars).

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Section: Related Workmentioning

confidence: 99%

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

Borges¹,

Valente²

2018

Journal of Systems and Software

231

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“…The problem of predicting remaining time has also been extensively studied in the context of software development processes. For example, Kikas et al predict issue resolution time in Github projects using static, dynamic, and contextual features. A later work by Rees‐Jones et al further emphasizes the importance of contextual features for predicting issue lifetime in Github projects.…”

Section: Related Workmentioning

confidence: 99%

Predicting process performance: A white‐box approach based on process models

Verenich

Dumas

Rosa

et al. 2019

J Software Evolu Process

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Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black-box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white-box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real-life event logs and compared against several baselines. KEYWORDS explainable artificial intelligence, flow analysis, predictive process monitoring, process mining, transparent models INTRODUCTIONPredictive business process monitoring techniques seek to predict the future state or properties of ongoing executions of a process based on models extracted from historical event logs. A wide range of predictive business process monitoring techniques have been proposed to predict, for example, compliance violations, 1,2 the next activity or the remaining sequence of activities of a process instance, 3,4 or quantitative process performance indicators, such the remaining cycle time of a process instance. 5-7 These predictions can be used to alert process workers to problematic process instances or to support resource allocation decisions, eg, to allocate additional resources to instances that are at risk of a deadline violation.This article addresses the problem of predicting quantitative process performance indicators, with a specific focus on predicting the remaining cycle time of ongoing process instances. Existing approaches to this problem adopt a "black-box" approach by building stochastic models or regression models which, given a process instance, predict the remaining execution time as a single scalar value, without seeking to explain this prediction in terms of more elementary components. Yet, quantitative performance indicators such as cost or time are aggregations of corresponding performance indicators of the activities composing the process. For example, the cycle time of a process instance with sequentially performed activities consists of the sum of the cycle time of the activities performed in that process instance. In this respect, existing known as flow analysis. 8 The idea of flow analysis is to estimate a quantitative performance indicator at the level of a process by aggregating the estimated values of this perfo...

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“…We chose this period of time because GHTorrent started data collection in 2012, and we need to collect about 10,000 projects (considering about the work load). Then, we deleted projects that are forks (recommended by [17,18]) and obtained 8,638 samples. In addition, we deleted projects that are not described in English or have been removed.…”

Section: A Standard Datasetmentioning

confidence: 99%

Automatic Detection of Public Development Projects in Large Open Source Ecosystems: An Exploratory Study on GitHub

Cheng¹,

Li²,

Li³

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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 -Hosting over 10 million of software projects, GitHub is one of the most important data sources to study behavior of developers and software projects. However, with the increase of the size of open source datasets, the potential threats to mining these datasets have also grown. As the dataset grows, it becomes gradually unrealistic for human to confirm quality of all samples. Some studies have investigated this problem and provided solutions to avoid threats in sample selection, but some of these solutions (e.g., finding development projects) require human intervention. When the amount of data to be processed increases, these semi-automatic solutions become less useful since the effort in need for human intervention is far beyond affordable. To solve this problem, we investigated the GHTorrent dataset and proposed a method to detect public development projects. The results show that our method can effectively improve the sample selection process in two ways: (1) We provide a simple model to automatically select samples (with 0.827 precision and 0.947 recall); (2) We also offer a complex model to help researchers carefully screen samples (with 63.2% less effort than manually confirming all samples, and can achieve 0.926 precision and 0.959 recall). Keywords-open source ecosystem; project sample selection; automated method; public development project;I.

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Using dynamic and contextual features to predict issue lifetime in GitHub projects

Cited by 59 publications

References 24 publications

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

Predicting process performance: A white‐box approach based on process models

Automatic Detection of Public Development Projects in Large Open Source Ecosystems: An Exploratory Study on GitHub

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