Showing papers by "James A. Jones published in 2020"
TL;DR: In this paper, the authors conduct an empirical study with fine-grained event data from 20 large open source projects hosted on GITHUB to investigate elite developers' contributing activities and their impacts on project outcomes.
Abstract: Open source developers, particularly the elite developers who own the administrative privileges for a project, maintain a diverse portfolio of contributing activities. They not only commit source code but also exert significant efforts on other communicative, organizational, and supportive activities. However, almost all prior research focuses on specific activities and fails to analyze elite developers’ activities in a comprehensive way. To bridge this gap, we conduct an empirical study with fine-grained event data from 20 large open source projects hosted on GITHUB. We investigate elite developers’ contributing activities and their impacts on project outcomes. Our analyses reveal three key findings: (1) elite developers participate in a variety of activities, of which technical contributions (e.g., coding) only account for a small proportion; (2) as the project grows, elite developers tend to put more effort into supportive and communicative activities and less effort into coding; and (3) elite developers’ efforts in nontechnical activities are negatively correlated with the project’s outcomes in terms of productivity and quality in general, except for a positive correlation with the bug fix rate (a quality indicator). These results provide an integrated view of elite developers’ activities and can inform an individual’s decision making about effort allocation, which could lead to improved project outcomes. The results also provide implications for supporting these elite developers.
14 citations
TL;DR: This paper proposes an approach for clustering crowdsourced test reports of mobile applications based on both textual and image features to assist the inspection procedure and employs Spatial Pyramid Matching (SPM) to measure the similarity of the screenshots and use the natural-language-processing techniques to compute the textual distance of test reports.
Abstract: Crowdsourced testing has been widely used to improve software quality as it can detect various bugs and simulate real usage scenarios. Crowdsourced workers perform tasks on crowdsourcing platforms and present their experiences as test reports, which naturally generates an overwhelming number of test reports. Therefore, inspecting these reports becomes a time-consuming yet inevitable task. In recent years, many text-based prioritization and clustering techniques have been proposed to address this challenge. However, in mobile testing, test reports often consist of only short test descriptions but rich screenshots. Compared with the uncertainty of textual information, well-defined screenshots can often adequately express the mobile application's activity views. In this paper, by employing image-understanding techniques, we propose an approach for clustering crowdsourced test reports of mobile applications based on both textual and image features to assist the inspection procedure. We employ Spatial Pyramid Matching (SPM) to measure the similarity of the screenshots and use the natural-language-processing techniques to compute the textual distance of test reports. To validate our approach, we conducted an experiment on 6 industrial crowdsourced projects that contain more than 1600 test reports and 1400 screenshots. The results show that our approach is capable of outperforming the baselines by up to 37% regarding the APFD metric. Further, we analyze the parameter sensitivity of our approach and discuss the settings for different application scenarios.
13 citations
27 Jun 2020
TL;DR: An large scale empirical study which characterizes elite developers' activity profiles and identifies the relationships between their effort allocations and project outcomes across five ecosystems reveals that elite developers in each ecosystem do behave in ecosystem-specific ways.
Abstract: OSS ecosystems promote code reuse, and knowledge sharing across projects within them. An ecosystem's developers often develop similar activity patterns which might impact project outcomes in an ecosystem-specific way. Since elite developers play critical roles in most OSS projects, investigating their behaviors at the ecosystem level becomes urgent. Thus, we aim to investigate elite developers' activities and their relationships with project outcomes (productivity and quality). We design an large scale empirical study which characterizes elite developers' activity profiles and identifies the relationships between their effort allocations and project outcomes across five ecosystems. Our current results and findings reveal that elite developers in each ecosystem do behave in ecosystem-specific ways. Further, we find that the elites' effort allocations on different activity categories are potentially correlated with project outcomes.