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SAST: A self-attention based method for skill translation in T-shaped expert finding

INFORMATION SCIENCES 2024年 680卷

作者： Fallahnejad, Zohreh Beigy, Hamid Sharif Univ Technol Dept Comp Engn Tehran Iran

Nowadays, organizations are seeking professionals who can both excel in their areas of expertise and collaborate effectively across different disciplines. This demand has given rise to the concept T-shaped experts who possess a deep understanding of one topic domain and a broad knowledge several others. This combination allows these professionals to be more creative, flexible, and adaptable in problem-solving by leveraging their diverse perspectives and experiences. To find Tshaped experts in any skill area, we need to measure how deep and wide their knowledge is. In this paper, we present a novel translation-based method to estimate each user's depth of knowledge in given skill area. The proposed method leverages a self-attention-based multi-label classification network to identify the most relevant translations for each skill that belongs to the given skill area. We utilize two new methods based on binary cross-entropy and focal loss to determine whether a user's expertise shape matches the T-shaped. We evaluate the proposed method using the standard benchmark datasets. The experimental results on three collections of the stackoverflow dataset demonstrate the superiority of the proposed methods in comparison with existing baselines.

关键词： T-shaped expert finding Translation method Self-attention technique Community question answering stackoverflow

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ConfigMiner: Identifying the Appropriate Configuration Options for Config-Related User Questions by Mining Online Forums

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2021年第12期47卷 2907-2918页

作者： Sayagh, Mohammed Hassan, Ahmed E. Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

While the behavior of a software system can be easily changed by modifying the values of a couple of configuration options, finding one out of hundreds or thousands of available options is, unfortunately, a challenging task. Therefore, users often spend a considerable amount of time asking and searching around for the appropriate configuration options in online forums such as stackoverflow. In this paper, we propose ConfigMiner, an approach to automatically identify the appropriate option(s) to config-related user questions by mining already-answered config-related questions in online forums. Our evaluation on 2,062 config-related user questions for seven software systems shows that ConfigMiner can identify the appropriate option(s) for a median of 83 percent (up to 91 percent) of user questions within the top-20 recommended options, improving over state-of-the-art approaches by a median of 130 percent. Besides, ConfigMiner reports the relevant options at a median rank of 4, compared to a median of 16-20.5 as reported by the state-of-the-art approaches.

关键词： Software systems Computer bugs Debugging Statistical analysis Task analysis Prediction algorithms Data mining Configuration user questions online forums stackoverflow

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Automated Error Log Resolution: A Case Study 21

Automated Error Log Resolution: A Case Study

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36th Annual ACM Symposium on Applied Computing (SAC)

作者： Fuller, Mark Brighton, Elizabeth Schiewe, Micah Das, Dipta Cerny, Tomas Tisnovsky, Pavel Baylor Univ Waco TX 76798 USA Red Hat Brno Czech Republic

ISBN: (纸本)9781450381048

Debugging and error resolution has become increasingly time-consuming and difficult for all domains of software development. Error logs have become very important when it comes to debugging and error resolution. To remedy the problems presented in the logs, typically, a search on online forums would shed light on the solution. We present a novel approach to utilizing these logs in conjunction with external Question and Answer forums to compute and expedite resolution by suggesting a solution to runtime errors. Since log format is non-standard and use cases can vary widely, our architecture allows for extreme customization for the intended ecosystem as well as a great degree of fine-tuning. We evaluated our solution in a case study and made our implementation open-source for the community.

关键词： Defect Resolution Error Logs Log Analysis stackoverflow

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Attention-based skill translation models for expert finding

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期193卷 116433-116433页

作者： Fallahnejad, Zohreh Beigy, Hamid Sharif Univ Technol Dept Comp Engn Tehran Iran

The growing popularity of community question answering websites can be seen by the growing number of users. Many methods are proposed to identify talented users in these communities, but many of them suffer from vocabulary mismatches. The solution to this problem can be found in translation approaches. The present paper proposes two translation methods for extracting more relevant translations. The proposed methods rely on the attention mechanism. The methods use multi-label classifiers that take each question as input and predict the skills related to the question. Using the attention mechanism, the model is able to focus on specific parts of the given input and predict the correct labels. The ultimate goal of these networks is to predict skills related to questions. Using word attention scores, we can find out how relevant a single word is to a particular skill. As a result of these attention scores, we obtain more relevant translations for each skill. We then use these translations to bridge the lexical gap and improve expert retrieval results. Extensive experiments on two large sub-collections of the stackoverflow dataset demonstrate that the proposed methods outperform the best baseline method by up to 14.11/% MAP improvement.

关键词： Expert finding Semantic matching Translation models stackoverflow

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An empirical study of COVID-19 related posts on Stack Overflow: Topics and technologies

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JOURNAL OF SYSTEMS AND SOFTWARE 2021年 182卷 111089-111089页

作者： Georgiou, Konstantinos Mittas, Nikolaos Chatzigeorgiou, Alexandros Angelis, Lefteris Aristotle Univ Thessaloniki Sch Informat Thessaloniki Greece Int Hellen Univ Dept Chem Kavala Greece Univ Macedonia Dept Appl Informat Thessaloniki Greece

The COVID-19 outbreak, also known as the coronavirus pandemic, has left its mark on every aspect of our lives and at the time of this writing is still an ongoing battle. Beyond the immediate global wide health response, the pandemic has triggered a significant number of IT initiatives to track, visualize, analyze and potentially mitigate the phenomenon. For individuals or organizations interested in developing COVID-19 related software, knowledge-sharing communities such as Stack Overflow proved to be an effective source of information for tackling commonly encountered problems. As an additional contribution to the investigation of this unprecedented health crisis and to assess how fast and how well the community of developers has responded, we performed a study on COVID-19 related posts in Stack Overflow. In particular, we profiled relevant questions based on key post features and their evolution, identified the most prominent technologies adopted for developing COVID-19 software and their interrelations and focused on the most persevering problems faced by developers. For the analysis of posts we employed descriptive statistics, Association Rule Graphs, Survival Analysis and Latent Dirichlet Allocation. The results reveal that the response of the developers' community to the pandemic was immediate and that the interest of developers on COVID-19 related challenges was sustained after its initial peak. In terms of the problems addressed, the results show a clear focus on COVID-19 data collection, analysis and visualization from/to the web, in line with the general needs for monitoring the pandemic. (C) 2021 Elsevier Inc. All rights reserved.

关键词： COVID-19 Pandemic stackoverflow Knowledge-sharing

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Intern retrieval from community question answering websites: A new variation of expert finding problem

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 181卷 115044-115044页

作者： Rostami, Peyman Neshati, Mahmood Shahid Beheshti Univ Fac Comp Sci & Engn Tehran Iran

In recent years, with the advent of new scientific and technical areas, hiring suitable interns has become very important for large companies. In internship programs, to reduce the risk of unsuccessful recruitment, companies are actively seeking those candidates who are ready to become skillful experts, and also incur the least financial costs. Therefore, generalists (i.e. Hyphen-shaped people) are the most suitable candidates for such positions. These candidates have general knowledge in the required skills of the position and do not have expertise in any other area. One of the environments that accurately reflect the knowledge of people is community question answering (CQA). This study is the first that focuses on retrieving interns from CQA websites. It uses the concepts of generalist and shape of expertise to identify suitable candidates for the internship programs of companies. Specifically, in this paper, we define the intern retrieval problem, in which given a set of required skills of an internship position, a ranking of candidates is generated so that the generalists who have general knowledge in those skills are retrieved in top ranks. We propose two retrieval models to address the problem. In order to evaluate the performance of these models, we introduce two specific measures (i.e. coverage and optimality). Our experiments on three test collections extracted from stackoverflow demonstrate the effectiveness of our models in comparison with several baselines.

关键词： Information retrieval Expert finding Internship Generalist Shape of expertise stackoverflow

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T-shaped grouping: Expert finding models to agile software teams retrieval

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 118卷 231-245页

作者： Rostami, Peyman Neshati, Mahmood Shahid Beheshti Univ Fac Comp Sci & Engn GC Tehran Iran

Agile team formation is an important requirement of software companies. Since the members of an agile team should be generalizing specialists (i.e. T-shaped experts), we need members who are specialist in a specific topic and have general knowledge in other topics of the team. Selecting such members results in an ideal team which is flexible, high-performing and low-cost. In this paper, we define the problem of agile team formation in which given a set of required skills of an agile team, the ideal output is a set of low-cost candidates who can collectively cover the required skills while they can effectively communicate with each other. We propose two retrieval models to address this problem and then we introduce three evaluation measures for assessment. These measures are coverage, communication and optimality. Our experiments on two test collections extracted from stackoverflow demonstrate the efficiency of our proposed models in comparison with several strong baselines. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Information retrieval Expert finding Agile methodologies Team formation T-shaped experts stackoverflow

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Library adoption in public software repositories

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JOURNAL OF BIG DATA 2019年第1期6卷 1页

作者： Krohn, Rachel Weninger, Tim Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

We study the the spread and adoption of libraries within Python projects hosted in public software repositories on GitHub. By modelling the use of Git pull, merge, commit, and other actions as deliberate cognitive activities, we are able to better understand the dynamics of what happens when users adopt new and cognitively demanding information. For this task we introduce a large corpus containing all commits, diffs, messages, and source code from 259,690 Python repositories (about 13% of all Python projects on Github), including all Git activity data from 89,311 contributing users. In this initial work we ask two primary questions: (1) What kind of behavior change occurs near an adoption event? (2) Can we model future adoption activity of a user? Using a fine-grained analysis of user behavior, we show that library adoptions are followed by higher than normal activity within the first 6 h, implying that a higher than normal cognitive effort is involved with an adoption. Further study is needed to understand the specific types of events that surround the adoption of new information, and the cause of these dynamics. We also show that a simple linear model is capable of classifying future commits as being an adoption or not, based on the commit contents and the preceding history of the user and repository. Additional work in this vein may be able to predict the content of future commits, or suggest new libraries to users.

关键词： Information adoption Software libraries GitHub Python stackoverflow Classification SVM Modelling Git Repository Commit Software development Cognitive science Text mining

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Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY 2019年第3期13卷 48-59页

作者： Singh, Abhishek Kumar Nagwani, Naresh Kumar Pandey, Sudhakar NIT Raipur Raipur Madhya Pradesh India Natl Inst Technol Raipur Raipur Madhya Pradesh India

Community question-answering (CQA) sites are popular as information-seeking platforms where users communicate to their peers. Security-related posts are gaining popularity with the rapid development of information technology in these sites and. CQA sites contains wide range of posts from classic cryptography to recently popular mobile security. Investigating such posts can be useful for researchers, teachers and developers. In this article, spectral clustering and frequent term-based summarization techniques are proposed for security related posts. The proposed method is developed in three stages. In the first stage, security related folksonomies are created and security post profile matrix is built with the help of tag frequency-inverse security post frequency. In the second stage, security related posts are grouped with help of spectral clustering algorithms. Finally, in the third stage, frequent terms are extracted from each cluster for security related post summarization with the help of frequent words and semantic similarity.

关键词： Community Question Answering Sites Frequent Words Security Semantic Similar stackoverflow Text Summarization

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Expert ranking techniques for online rated forums

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COMPUTERS IN HUMAN BEHAVIOR 2019年 100卷 168-176页

作者： Faisal, Muhammad Shahzad Daud, Ali Akram, Abubakr Usman Abbasi, Rabeeh Ayaz Aljohani, Naif Radi Mehmood, Irfan Int Islamic Univ Dept Comp Sci & Software Islamabad Pakistan COMSATS Inst IT Dept Comp Sci Attock Pakistan KAU FCIT Dept Informat Syst Jeddah Saudi Arabia Quaid I Azam Univ Dept Comp Sci Islamabad Pakistan Sejong Univ Dept Software Seoul South Korea

Web 2.0 or social web applications such as online discussion forums, blogs and Wikipedia have improved knowledge sharing by providing an environment in which users can generate and find their favorite content in, a flexible way. With the passage of time, online discussion forums accumulate a huge amount of content and this can introduce issues of content quality and user credibility. A poor-quality answer in a discussion forum indicates the presence of unprofessional or unqualified users;therefore, a priority is to find experts or reputable users. Most of the existing expert-ranking approaches consider basic features, such as the total number of answers provided by a user, but ignore the quality and consistency of the user's answer. In this paper, expert-ranking techniques using g-index are proposed, and are applied to a stackoverflow forum dataset. Three techniques are proposed including Exp-PC, Rep-FS and Weighted Exp-PC. Exp-PC is an adaptation of g-index for ranking experts in stackoverflow forum. In Rep-FS, several features like voters reputation, vote ratio are proposed to measure users' expertise while Weighted Exp-PC computes user expertise by combining their Exp-PC and Rep-FS scores. We measure users' reputation and expertise according to both the quality of their answer and their consistency in providing quality answers. The experimental results of the proposed expert-ranking techniques, Exp-PC and Weighted Exp-PC in particular, validate that these methods identify genuine experts in a more effective way.

关键词： Expert finding Online rated forums stackoverflow Information retrieval Social media

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