There is an increasing interest shown by researchers and developers in reducing the battery consumption of Android applications. Recently, the battery optimization features such as doze mode, app standby, background e...
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There is an increasing interest shown by researchers and developers in reducing the battery consumption of Android applications. Recently, the battery optimization features such as doze mode, app standby, background execution limits, and background location limits were introduced in the form of API changes. According to the API changes, application developers have to change their source code to manage the behavioral changes caused by operating system limitations. These battery optimization features are evolving rapidly, and the apps show unexpected behaviors until updating the source code. Also, developers find it difficult to cope with the changes. Therefore, there is a need to understand the behavioral changes, application developer's perceptions, and response patterns on the API changes to plan upcoming battery optimization features. In this article, we have collected the relevant github issues from 225 open-source Android repositories and performed a thematic analysis of collected data. This study analyzes the 391 related issues to answer three research questions. This study's important finding is that developers often post issues related to delayed app notifications, inconsistent background location updates, and suspended background tasks, and so on. We found that library developers are showing a quick response to API changes compared with application developers.
The full integration of online repositories in contemporary software development promotes remote work and collaboration. Apart from the apparent benefits, online repositories offer a deluge of data that can be utilize...
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ISBN:
(纸本)9781450375177
The full integration of online repositories in contemporary software development promotes remote work and collaboration. Apart from the apparent benefits, online repositories offer a deluge of data that can be utilized to monitor and improve the software development process. Towards this direction, we have designed and implemented a platform that analyzes data from github in order to compute a series of metrics that quantify the contributions of project collaborators, both from a development as well as an operations (communication) perspective. We analyze contributions throughout the projects' lifecycle and track the number of coding violations, this way aspiring to identify cases of software development that need closer monitoring and (possibly) further actions to be taken. In this context, we have analyzed the 3000 most popular github Java projects and provide the data to the community.
Most software teams nowadays host their projects online and monitor software development in the form of issues/tasks. This process entails communicating through comments and reporting progress through commits and clos...
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ISBN:
(纸本)9781728189130
Most software teams nowadays host their projects online and monitor software development in the form of issues/tasks. This process entails communicating through comments and reporting progress through commits and closing issues. In this context, assigning new issues, tasks or bugs to the most suitable contributor largely improves efficiency. Thus, several automated issue assignment approaches have been proposed, which however have major limitations. Most systems focus only on assigning bugs using textual data, are limited to projects explicitly using bug tracking systems, and may require manually tuning parameters per project. In this work, we build an automated issue assignment system for github, taking into account the commits and issues of the repository under analysis. Our system aggregates feature probabilities using a neural network that adapts to each project, thus not requiring manual parameter tuning. Upon evaluating our methodology, we conclude that it can be efficient for automated issue assignment.
Context: Machine Learning Operations (MLOps) has emerged as a crucial technology for addressing the challenges of designing and maintaining productive ML applications. The widespread adoption of MLOps makes it essenti...
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Context: Machine Learning Operations (MLOps) has emerged as a crucial technology for addressing the challenges of designing and maintaining productive ML applications. The widespread adoption of MLOps makes it essential to identify the problems faced by MLOps practitioners. However, there has been relatively little research in this area. Objectives: To fill this research gap and gain an understanding of the interests and difficulties encountered by MLOps practitioners. Methods: We mine discussion data from the online Q&A forum, Stack Overflow, and github projects, and analyze 6345 posts and 2103 issues. Results: We construct the first taxonomy of MLOps problems in practice, consisting of 5 categories and 19 topics. We also investigate the evolution and characteristics (difficulty and sentiment) of these topics, distill 12 frequent solutions for different MLOps problems, and design an MLOps knowledge exploration tool, MLOps-KET. Conclusion: We find that practitioners face diverse challenges when performing MLOps practices and that the focus of their discussions changed over time. Our study contributes to the MLOps research and development community by providing implications for different audiences and guidance for future support of relevant techniques and tools.
Software development generally produces programs with two caveats, i.e., some codes buggy and certain features incomplete. This make bugs and features two of the most important factors in software development. The inc...
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ISBN:
(纸本)9781538669877
Software development generally produces programs with two caveats, i.e., some codes buggy and certain features incomplete. This make bugs and features two of the most important factors in software development. The inception of issue tracking systems, provides an efficient way to collect valuable feedback from the user community. People can easily report bugs or features by submitting an issue report. However, disparate projects with different goals and needs may converge to different treatments to these two types of issues. Conversely, different treatments may bring different project outcomes. Many studies have explored bugs and features separately, few research has investigated the resolution differences between them from the developers' perspective. In this paper, we present a preliminary study of the resolution differences between the bugs and features in open-source projects by using quantitative methods. We collected and analyzed data from 272 github projects. By building two regression models, we explored how bugs and features differ in discussion length and resolution latency. Furthermore, we performed a qualitative study to compare the difference of description diversity between bugs and features. Our study results show that there are some differences in developers' handling of bugs and features. Finally, we distill some implications for different stakeholders.
Finding qualities in requirements-related information is important. Quality requirements are central in building reliable software. However, an isolated identification of qualities is not enough;the impact of an indiv...
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ISBN:
(纸本)9781538683613
Finding qualities in requirements-related information is important. Quality requirements are central in building reliable software. However, an isolated identification of qualities is not enough;the impact of an individual quality may compete with another quality requirement. This can be perceived in the example: "the trade-off between usability and security is not completely secure". This work studies the use of sentiment analysis to help finding relations among qualities. We will focus on usability and will depart from available NFR (Non-Functional Requirements) catalogues for this specific NFR. The catalogues will be the seed for building a corpus, based on queries in github's issues. We are aiming to find a list of sentiment expressions that will characterize important relations among usability and other qualities, through the analysis of our mining study. We will contextualize our results in connection with recent work on sentiment analysis for RE, focusing on the specific case of NFRs.
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