A user evaluation of synchronous collaborative software engineering tools

Cook, C.; Irwin, Warwick; Churcher, Neville

doi:10.1109/apsec.2005.22

Cited by 15 publications

(6 citation statements)

References 10 publications

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“…Participants who attempted and/or completed the task were asked to fill out a survey about their experience, including the development environment they used, how much time they spent on each sub-task, and which activities they did and functionality they used as part of the task and which of these were most important. They were also asked to provide open-ended feedback on different aspects, and to report how demanding the task was using the NASA-TLX Task Load Index [38], a workload assessment that is widely used in usability evaluations in software engineering and other domains [22,75]. Participants indicate on a scale the temporal demand, mental demand, and effort required by the task, their perceived performance, and their frustration.…”

Section: General Proceduresmentioning

confidence: 99%

Enabling Collaborative Data Science Development with the Ballet Framework

Smith

Cito

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, the first lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to software and ML performance validation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.CCS Concepts: • Human-centered computing → Collaborative and social computing systems and tools; Empirical studies in collaborative and social computing; • Computing methodologies → Machine learning; • Software and its engineering → Collaboration in software development.

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Section: General Proceduresmentioning

confidence: 99%

Enabling Collaborative Data Science Development with the Ballet Framework

Smith

Cito

et al. 2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

show abstract

“…Participants who completed or attempted the task were asked to complete a survey about their experience, including the development environment they used, time spent by sub-task, activities/functionality they did/used as part of the task and which were most important, and open-ended feedback on different aspects. They were also asked about how demanding the task was using the NASA-TLX Task Load Index (Hart & Staveland, 1988), a workload assessment that is widely used in usability evaluations in software engineering and other domains (Cook et al, 2005;Salman & Turhan, 2018).…”

Section: Case Study Designmentioning

confidence: 99%

Enabling Collaborative Data Science Development with the Ballet Framework

Smith,

Cito,

et al. 2020

Preprint

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While the open-source model for software development has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small groups. We describe challenges to scaling data science collaborations and present a novel ML programming model to address them. We instantiate these ideas in Ballet, a lightweight software framework for collaborative open-source data science and a cloud-based development environment, with a plugin for collaborative feature engineering. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to an ML evaluation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct an extensive case study analysis of a real-world income prediction problem, and discuss implications for collaborative projects.

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“…Even though, we have not explicitly done an experiment to evaluate the usefulness of group in collaboration, we researched for other works that have evaluate the usefulness of group collaboration. Cook et al [20] presented a user evaluation of collaborative tools for software engineering. They used a post experiment questionnaire to evaluate the experiments qualitative aspects.…”

Section: Usefulness Of Group Collaborationmentioning

confidence: 99%

Supporting A Flexible Grouping Mechanism for Collaborating Engineering Teams

Kanakis

Fischer²,

Khelladi³

et al. 2019

2019 ACM/IEEE 14th International Conference on Global Software Engineering (ICGSE)

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Most engineering tools do not provide much support for collaborating teams and today's engineering knowledge repositories lack flexibility and are limited. Engineering teams have different needs and their team members have different preferences on how and when to collaborate. These needs may depend on the individual work style, the role an engineer has, and the tasks they have to perform within the collaborating group. However, individual collaboration is insufficient and engineers need to collaborate in groups. This work presents a collaboration framework for collaborating groups capable of providing synchronous and asynchronous mode of collaboration. Additionally, our approach enables engineers to mix these collaboration modes to meet the preferences of individual group members. We evaluate the scalability of this framework using four real life large collaboration projects. These projects were found from GitHub and they were under active development by the time of evaluation. We have tested our approach creating groups of different sizes for each project. The results showed that our approach scales to support every case for the groups created. Additionally, we scouted the literature and discovered studies that support the usefulness of different groups with collaboration styles.

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A user evaluation of synchronous collaborative software engineering tools

Cited by 15 publications

References 10 publications

Enabling Collaborative Data Science Development with the Ballet Framework

Enabling Collaborative Data Science Development with the Ballet Framework

Enabling Collaborative Data Science Development with the Ballet Framework

Supporting A Flexible Grouping Mechanism for Collaborating Engineering Teams

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