Predicting pull request completion time: a case study on large scale cloud services

Maddila, Chandra; Bansal, Chetan; Nagappan, Nachiappan

doi:10.1145/3338906.3340457

Cited by 39 publications

(31 citation statements)

References 22 publications

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“…The average accuracy of our approach was of 45.28% for predicting pull request lifetime, which meant an average normalized improvement of 14.68% in relation to the majority class (the highest percentage of pull requests in the same time interval) and 6.49% compared with the Approach A proposed by Yu et al 5 Maddila et al 4 built a pull request lifetime prediction model using 28 attributes (19 numerical and nine categorical) and 2875 pull requests with lifetime between 24 h and 2 weeks. The predictions were validated by developers from Microsoft that work on ten different projects.…”

Section: Related Workmentioning

confidence: 78%

“…However, the approach proposed by Maddila et al 4 explores data sourced from industrial projects that have different context, attributes, and workflows from open-source projects. Some attributes are unique to Microsoft repositories, such as the age of the developer on the Microsoft team and whether the pull request changed a project configuration file in the C# language.…”

Section: Related Workmentioning

confidence: 99%

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Predicting the lifetime of pull requests in open‐source projects

Júnior

Soares

Plastino

et al. 2021

J Software Evolu Process

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A recent survey using industrial projects has shown that providing an estimate of the lifetime of pull requests to developers helps to speed up their conclusion. Previous work has explored pull request lifetime prediction in open‐source projects using regression techniques but with a broad margin of error. The first objective of our work was to reduce the average error rate of the prediction obtained by the regression techniques so far. We performed experiments with different regression techniques and achieved a significant decrease in the mean error rate. The second objective of our work was to obtain a more effective and useful predictive model that can classify pull requests according to five discrete time intervals. We proposed new predictive attributes for the estimation of the time intervals and employed attribute selection strategies to identify subsets of attributes that could improve the predictive behavior of the classifiers. Our classification approach achieved the best accuracy in all the 20 projects evaluated in comparison with the literature. The average accuracy was of 45.28% to predict pull request lifetime, with an average normalized improvement of 14.68% in relation to the majority class and 6.49% in relation to the state‐of‐the‐art.

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Section: Related Workmentioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Predicting the lifetime of pull requests in open‐source projects

Júnior

Soares

Plastino

et al. 2021

J Software Evolu Process

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“…Machine learning models have gained popularity as an alternative to model-based estimation methods. They have achieved promising results in estimating effort for software projects [34]- [38], and predicting the elapsed time required for bug-fixing or resolving an issue [39]- [44]. Our study of factors affecting schedule deviation in epics can provide further insights into important effort drivers that can contribute to the improvement of estimation methods.…”

Section: Factors Influencing Software Development Effortmentioning

confidence: 92%

Factors Affecting On-Time Delivery in Large-Scale Agile Software Development

Kula

Greuter²,

Deursen

et al. 2022

IIEEE Trans. Software Eng.

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Late delivery of software projects and cost overruns have been common problems in the software industry for decades. Both problems are manifestations of deficiencies in effort estimation during project planning. With software projects being complex socio-technical systems, a large pool of factors can affect effort estimation and on-time delivery. To identify the most relevant factors and their interactions affecting schedule deviations in large-scale agile software development, we conducted a mixed-methods case study at ING: two rounds of surveys revealed a multitude of organizational, people, process, project and technical factors which were then quantified and statistically modeled using software repository data from 185 teams. We find that factors such as requirements refinement, task dependencies, organizational alignment and organizational politics are perceived to have the greatest impact on on-time delivery, whereas proxy measures such as project size, number of dependencies, historical delivery performance and team familiarity can help explain a large degree of schedule deviations. We also discover hierarchical interactions among factors: organizational factors are perceived to interact with people factors, which in turn impact technical factors. We compose our findings in the form of a conceptual framework representing influential factors and their relationships to on-time delivery. Our results can help practitioners identify and manage delay risks in agile settings, can inform the design of automated tools to predict schedule overruns and can contribute towards the development of a relational theory of software project management.

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“…BabiaXR is open source: Its source code is available on GitLab 8 and it can be installed with npm. 9 For the development of the on-screen participants, we will use Kibana. Kibana is a free and open frontend application that sits on top of the Elastic Stack, providing search and data visualization capabilities for data indexed in ElasticSearch.…”

Section: Toolsmentioning

confidence: 99%

“…The field of analysis for our experiment will be pull request activity. Pull request, as part of modern code review [1,16], is a software development activity that has been widely researched by academia in the last years [8,9,18]. It is of major interest to industry and practitioners as it is very human-intensive and often the cause of bottlenecks and inefficiencies [15].…”

Section: Introductionmentioning

confidence: 99%

To VR or not to VR: Is virtual reality suitable to understand software development metrics?

Moreno-Lumbreras,

Robles,

Izquierdo-Cortázar

et al. 2021

Preprint

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Background/Context: Currently, the usual interface for visualizing data is based on 2-D screens. Recently, devices capable of visualizing data while immersed in VR scenes are becoming common. However, it has not been studied in detail to which extent these devices are suitable for interacting with data visualizations in the specific case of data about software development.Objective/Aim: In this registered report, we propose to answer the following question: "Is comprehension of software development processes, via the visualization of their metrics, better when presented in VR scenes than in 2D screens?" In particular, we will study if answers obtained after interacting with visualizations presented as VR scenes are more or less correct than those obtained from traditional screens, and if it takes more or less time to produce those answers. Method: We will run an experiment with volunteer subjects from several backgrounds. We will have two setups: an on-screen application, and a VR scene. Both will be designed to be as much equivalent as possible in terms of the information they provide. For the former, we use a commercial-grade set of Kibana-based interactive dashboards that stakeholders currently use to get insights. For the latter, we use a set of visualizations similar to those in the on-screen case, prepared to provide the same set of data using the museum metaphor in a VR room. The field of analysis will be related to modern code review, in particular pull request activity. The subjects will try to answer some questions in both setups (some will work first in VR, some on-screen), which will be presented to them in random order. To draw results, we will compare and statistically analyze both the correctness of their answers, and the time spent until they are produced.

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Predicting pull request completion time: a case study on large scale cloud services

Cited by 39 publications

References 22 publications

Predicting the lifetime of pull requests in open‐source projects

Predicting the lifetime of pull requests in open‐source projects

Factors Affecting On-Time Delivery in Large-Scale Agile Software Development

To VR or not to VR: Is virtual reality suitable to understand software development metrics?

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