Hybrid Labels Are the New Measure!

Nayebi, Maleknaz; Kabeer, Shaikh Jeeshan; Ruhe, Guenther; Carlson, Chris; Chew, Francis

doi:10.1109/ms.2017.4541048

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…As a case study, we evaluated ESSMArT using data from the development of a Health Communication system offered by a company called BrightSquid. Analyzing customer change requests is an important part of their development process [33]. In particular, from analyzing a set of 4,114 former user requests, we answered the following research questions: RQ1: Automated condensing of user requests -Among existing state-of-the art techniques for condensing user requests by extractive summarization, which one works best in terms of F1 accuracy?…”

Section: Introductionmentioning

confidence: 99%

ESSMArT way to manage customer requests

et al. 2019

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Quality and market acceptance of software products is strongly influenced by responsiveness to user requests. Once a request is received from a customer, decisions need to be made if the request should be escalated to the development team. Once escalated, the ticket must be formulated as a development task and be assigned to a developer. To make the process more efficient and reduce the time between receiving and escalating the user request, we aim to automate of the complete user request management process. We propose a holistic method called ESSMArT. The methods performs text summarization, predicts ticket escalation, creates the title and content of the ticket used by developers, and assigns the ticket to an available developer. We internally evaluated the method by 4,114 user tickets from Brightsquid and their secure health care communication platform Secure-Mail. We also perform an external evaluation on the usefulness of the approach. We found that supervised learning based on context specific data performs best for extractive summarization. For predicting escalation of tickets, Random Forest trained on a combination of conversation and extractive summarization is best with highest precision (of 0.9) and recall (of 0.55). From external evaluation we found that ESSMArT provides suggestions that are 71% aligned with human ones. Applying the prototype implementation to 315 user requests

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Section: Introductionmentioning

confidence: 99%

ESSMArT way to manage customer requests

et al. 2019

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“…The research presented here is part of a long term project with BrightSquid to improve their software processes. For example we developed ESSMArT for automatic escalation and summarization of user requests [19] and implemented a tagging method for better estimation of effort needed to close a change request [20]. The research reported here is a second major step towards our goal of Intelligent Change Management.…”

Section: Discussionmentioning

confidence: 99%

A Longitudinal Study of Identifying and Paying Down Architecture Debt

Nayebi

Cai

Kazman

et al. 2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

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Architectural debt is a form of technical debt that derives from the gap between the architectural design of the system as it "should be" compared to "as it is". We measured architecture debt in two ways: 1) in terms of system-wide coupling measures, and 2) in terms of the number and severity of architectural flaws. In recent work it was shown that the amount of architectural debt has a huge impact on software maintainability and evolution. Consequently, detecting and reducing the debt is expected to make software more amenable to change. This paper reports on a longitudinal study of a healthcare communications product created by Brightsquid Secure Communications Corp. This start-up company is facing the typical trade-off problem of desiring responsiveness to change requests, but wanting to avoid the ever-increasing effort that the accumulation of quick-anddirty changes eventually incurs. In the first stage of the study, we analyzed the status of the "before" system, which indicated the impacts of change requests. This initial study motivated a more in-depth analysis of architectural debt. The results of this analysis were used to motivate a comprehensive refactoring of the software system. The third phase of the study was a follow-on architectural debt analysis which quantified the improvements made. Using this quantitative evidence, augmented by qualitative evidence gathered from in-depth interviews with Brightsquid's architects, we present lessons learned about the costs and benefits of paying down architecture debt in practice.

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“…Labels have different characteristics, whether created by either humans or machines [23]. They could apply to any related function, including reuse, management, and re-finding information [26] in many software artifacts such as architecture, components, documentation, testing, and others [28].…”

Section: Labelsmentioning

confidence: 99%

Label it be! A large-scale study of issue labeling in modern open-source repositories

Joselito¹,

Boechat²,

Machado³

2021

Preprint

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In a wave of growth, open-source projects need to modernize and change how they deal with processes, methods, and communication with their contributors. We could observe that open-source projects are constantly evolving to improve their management of the entire community. Starting with community communication, software development, managing open-source projects faces crucial challenges. One of the enabling environments that open-source communities found to achieve community communications objectives was code repositories with integration with issue trackers. Using issue trackers in their projects should encompass an infrastructure capable of hosting the project source code and community participation. Some issue trackers use a structure in which the issue's title and description are the key information. However, we have observed a slight change in this strategy over the years, as more and more data are fundamental to solving the issue. For example, labeling the issues could enable users to provide the issue with more contextual information. By understanding how modern issue trackers handle issue labeling, this study analyzes the impact, engagement, and influence that labels have on the Github repositories, based on a database of 10,673,459 issues mined from 13,280 repositories in 180 Github featured topics. We found that 78.75% of the repositories label their issues, with more adherence from those repositories that contain big numbers of issues. The labeling practice is essential and prioritized as a first step in the issue resolution process in 65.91% of the first events. Issues with labels draw more attention and impact by collecting more subscribers, assigns, and comments, helping to engage contributors to the resolution.

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Hybrid Labels Are the New Measure!

Cited by 12 publications

References 8 publications

ESSMArT way to manage customer requests

ESSMArT way to manage customer requests

A Longitudinal Study of Identifying and Paying Down Architecture Debt

Label it be! A large-scale study of issue labeling in modern open-source repositories

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