Building and exploiting context on the web

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Exploring the Use of Labels to Categorize Issues in Open-Source Software Projects

International audienceReporting bugs, asking for new features and in general giving any kind of feedback is a common way to contribute to an Open-Source Software (OSS) project. This feedback is generally reported in the form of new issues for the project, managed by the so-called issue-trackers. One of the features provided by most issue-trackers is the possibility to define a set of labels/tags to classify the issues and, at least in theory, facilitate their management. Nevertheless, there is little empirical evidence to confirm that taking the time to categorize new issues has indeed a beneficial impact on the project evolution. In this paper we analyze a population of more than three million of GitHub projects and give some insights on how labels are used in them. Our preliminary results reveal that, even if the label mechanism is scarcely used, using labels favors the resolution of issues. Our analysis also suggests that not all projects use labels in the same way (e.g., for some labels are only a way to prioritize the project while others use them to signal their temporal evolution as they move along in the development workflow). Further research is needed to precisely characterize these label "families" and learn more the ideal application scenarios for each of them

Motivators of adopting social computing in global software development: Initial results

context: Real-time collaboration is critical for developing high quality software systems at low cost in a geographically distributed Global Software Development (GSD) environment. It is anticipated that emerging Social Computing tools can play an important role in facilitating realtime effective collaboration among teams working in the GSD. Objective: The objective of this research paper is to identify motivators for adopting social computing in GSD organizations. Method: We adopted a Systematic Literature Review (SLR) approach by applying customized search strings derived from our research questions. Results: We have identified factors such as real-time communication and coordination, information sharing, knowledge acquisition and expert feedback as key motivators for adoption of social computing in GSD. Conclusion: Based on the SLR results, we suggest that GSD organizations should embrace social computing as a tool for real-time collaboration between distributed GSD teams. The results of this initial study also suggest the need for developing the social computing strategies and policies to guide the effective social computing adoption by GSD teams

Meta-tools for software language engineering : a flexible collaborative modeling language for efficient telecommunications service design

International audienceThe increasingly competitive environment pressures telecommunications service providers to reduce their concept-to-market time. This time is influenced by a multitude of factors. For the benefit of telecom service designers, this paper focuses on increasing the degree of automation, offering team collaboration capabilities and bridging heterogeneous technologies. To address these factors, we propose a model-based meta-tool approach, which rapidly and iteratively generates particular tools for software languages. Each language is specific to one of the viewpoints involved in the definition of a service, as identified in the Intelligent Network Conceptual Model. A flexible language prototype for service designers, that blends a higher degree of formality with creative freedom, has already been implemented. The integration of first collaboration capabilities, defined and tooled, into this language, by including the rationale behind the designers' decisions, is currently being pursued. A second language prototype, for network designers, together with syntactic and semantic (partial) automatic interoperability between these two viewpoints, are also proposed

Exploring the Use of Labels to Categorize Issues in Open-Source Software Projects

Abstract-Reporting bugs, asking for new features and in general giving any kind of feedback is a common way to contribute to an Open-Source Software (OSS) project. This feedback is generally reported in the form of new issues for the project, managed by the so-called issue-trackers. One of the features provided by most issue-trackers is the possibility to define a set of labels/tags to classify the issues and, at least in theory, facilitate their management. Nevertheless, there is little empirical evidence to confirm that taking the time to categorize new issues has indeed a beneficial impact on the project evolution. In this paper we analyze a population of more than three million of GitHub projects and give some insights on how labels are used in them. Our preliminary results reveal that, even if the label mechanism is scarcely used, using labels favors the resolution of issues. Our analysis also suggests that not all projects use labels in the same way (e.g., for some labels are only a way to prioritize the project while others use them to signal their temporal evolution as they move along in the development workflow). Further research is needed to precisely characterize these label "families" and learn more the ideal application scenarios for each of them

Paths to more effective personal information management

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 256-272).With the widespread availability of digital tools for storing, accessing, and sharing information, why is so much information still lost, forgotten, or kept on paper? The work in this thesis finds that such disorganization results from problems in the designs of the personal information management (PIM) tools in common use today. Such problems impede information capture, force many information forms to be left out, and cause information to be forgotten. How can these problems be mitigated? Our Information Scraps study identifies the need to support more diverse kinds of information, while conserving time, attention, and memory for retained information items. Our first approach to achieving these goals is to eliminate the artificial separation and homogeneity that structured PIM tools impose, so that arbitrary information can be captured in any way desired. A two-year study of List-it, our short-note-taking tool, discovers that people keep notes serving 5 primary roles: reminders, reference items, progress trackers, places to think, and archives of personal value. The second reintroduces structured data to support more effective use and management of information collections. Jourknow addresses the manageability of large note collections with lightweight-structured note contents and contextual retrieval, the access of notes by the contexts and activities at the time of creation. Poyozo reinforces recollection of previously seen information, by providing visualizations of all of a person's past information activities. Finally, Atomate addresses the challenge of managing the ever-increasing deluge of new information, by letting people delegate to software behaviors actions to be automatically taken when new information arrives. These studies identify critical needs of PIM tools and offer viable solutions.by Max Goodwin Van Kleek.Ph.D

Social Knowledge Environments

Knowledge management represents a key issue for both information systems’ academics and practitioners, including those who have become disillusioned by actual results that fail to deliver on exaggerated promises and idealistic visions. Social software, a tremendous global success story, has prompted similarly high expectations regarding the ways in which organizations can improve their knowledge handling. But can these expectations be met, whether in academic research or the real world? This article seeks to identify current research trends and gaps, with a focus on social knowledge environments. The proposed research agenda features four focal challenges: semi-permeable organizations, social software in professional work settings, crowd knowledge, and crossborder knowledge management. Three solutions emerge as likely methods to address these challenges: designoriented solutions, analytical solutions, and interdisciplinary dialogue

EnTagRec(++): An enhanced tag recommendation system for software information sites

Software engineers share experiences with modern technologies using software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. Tags help to improve the organization of questions and simplify the browsing of questions for users. However, tags assigned to objects tend to be noisy and some objects are not well tagged. For instance, 14.7% of the questions that were posted in 2015 on Stack Overflow needed tag re-editing after the initial assignment. To improve the quality of tags in software information sites, we propose EnTagRec++, which is an advanced version of our prior work EnTagRec. Different from EnTagRec, EnTagRec++ does not only integrate the historical tag assignments to software objects, but also leverages the information of users, and an initial set of tags that a user may provide for tag recommendation. We evaluate its performance on five software information sites, Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode. We observe that even without considering an initial set of tags that a user provides, it achieves Recall@5 scores of 0.821, 0.822, 0.891, 0.818 and 0.651, and Recall@10 scores of 0.873, 0.886, 0.956, 0.887 and 0.761, on Stack Overflow, Ask Ubuntu, Ask Different, Super User, and Freecode, respectively. In terms of Recall@5 and Recall@10, averaging across the 5 datasets, it improves upon TagCombine, which is the prior state-of-the-art approach, by 29.3% and 14.5% respectively. Moreover, the performance of our approach is further boosted if users provide some initial tags that our approach can leverage to infer additional tags: when an initial set of tags is given, Recall@5 is improved by 10%