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

作者： Silveira, Karina Kohl Musse, Soraia Manssour, Isabel H. Vieira, Renata Prikladnicki, Rafael Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre RS Brazil

ISBN: (纸本)9781728117645

Diversity is being intensively discussed by different knowledge areas of society and discussions in Software Engineering, are increasing as well. There are unconscious bias and lack of representativeness and when we talk about characteristics as ethnicity and gender, to mention a few. Studies say that diversity builds better teams and delivers better results, among other benefits. stackoverflow is a popular community questionanswer forum, with a high engagement of software developers. Yearly, they apply a survey, present straightforward results, and made the anonymized results available for download, so it is possible to perform additional analysis beyond the original ones. Using data visualization techniques, we analyzed 2018 data to extract some insights about how genders see their confidence in programming skills. Results show that respondents from underrepresented groups tend to believe they are not as good as their peers. From that, we propose a discussion about the unconscious bias, stereotypes, and impostor syndrome and how to provide support on that.

关键词： Software engineering Software Development Diversity Data Visualization stackoverflow

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Searching stackoverflow Questions with Multi-Faceted Categorization 18

Searching StackOverflow Questions with Multi-Faceted Categor...

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10th Asia-Pacific Symposium on Internetware (Internetware)

作者： Liu, Mingwei Peng, Xin Jiang, Qingtao Marcus, Andrian Yang, Junwen Zhao, Wenyun Fudan Univ Sch Comp Sci Shanghai Peoples R China Univ Texas Dallas Dept Comp Sci Richardson TX 75083 USA Fudan Univ Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9781450365901

stackoverflow provides answers for a huge number of software development questions that are frequently encountered by developers. However, searching relevant questions in stackoverflow is not always easy using the keyword based search engine provided by stackoverflow. A software development question can be characterized by multiple attributes, such as, its concern (e.g., configuration problem, error handling, sample code, etc.), programming language, operating system, and involved middleware, framework, library and software technology. We propose a multi-faceted and interactive approach for searching stackoverflow questions (called MFISSO), which leverages these attributes of the questions. Our approach starts with an initial keyword-based query and extracts a multifaceted categorization from all the candidate questions using natural language processing and data mining. It then allows developers to iteratively refine the search results through an interactive process. We evaluated an implementation of MFISSO in a controlled experiments with 20 computing students, solving ten software development tasks using stackoverflow. The experiment shows that MFISSO can help developers find relevant questions faster and with higher accuracy.

关键词： Information Retrieval stackoverflow Multi-faceted Categorization

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On code reuse from stackoverflow: An exploratory study on Android apps

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INFORMATION AND SOFTWARE TECHNOLOGY 2017年 88卷 148-158页

作者： Abdalkareem, Rabe Shihab, Emad Rilling, Juergen Concordia Univ Data Driven Anal Software DAS Lab Montreal PQ Canada Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada

Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that stackoverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on stackoverflow. However, little work studies the impact of code reuse from Stack Overflow. Objective: To better understand the impact of code reuse from stackoverflow, we perform an exploratory study focusing on code reuse from stackoverflow in the context of mobile apps. Specifically, we investigate how much, why, when, and who reuses code. Moreover, to understand the potential implications of code reuse, we examine the percentage of bugs in files that reuse stackoverflow code. Method: We perform our study on 22 open source Android apps. For each project, we mine their source code and use clone detection techniques to identify code that is reused from stackoverflow. We then apply different quantitative and qualitative methods to answer our research questions. Results: Our findings indicate that 1) the amount of reused stackoverflow code varies for different mobile apps, 2) feature additions and enhancements in apps are the main reasons for code reuse from stackoverflow, 3) mid-age and older apps reuse stackoverflow code mostly later on in their project lifetime and 4) that in smaller teams/apps, more experienced developers reuse code, whereas in larger teams/apps, the less experienced developers reuse code the most. Additionally, we found that the percentage of bugs is higher in files after reusing code from stackoverflow. Conclusion: Our results provide insights on the potential impact of code reuse from stackoverflow on mobile apps. Furthermore, these results can benefit the research community in developing new techniques and tools to facilitate and improve code reuse from stackoverflow. (C) 2017 Elsevier B.V. All rights reserved.

关键词： stackoverflow Mobile app Code reuse

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基于stackoverflow的软件异常抛出问题的解决方案推荐

基于StackOverflow的软件异常抛出问题的解决方案推荐

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作者：刘晓宁上海交通大学

学位级别：硕士

异常是一种用于处理软件或信息系统中出现的特殊状况的程序语言机制。现代程序语言,如Java、C#等,大多都具有完善的异常处理流程与规范。开发人员用于处理这些异常与错误的常用方式是利用Google与Bing等通用搜索引擎进行检索,找到建议... 详细信息

异常是一种用于处理软件或信息系统中出现的特殊状况的程序语言机制。现代程序语言,如Java、C#等,大多都具有完善的异常处理流程与规范。开发人员用于处理这些异常与错误的常用方式是利用Google与Bing等通用搜索引擎进行检索,找到建议的解决方案。然而,通用搜索引擎由于检索语句长度以及匹配算法的限制,并不能很好的理解与处理异常信息的查询,结果往往并不理想。针对软件异常缺陷处理方案的智能推荐相对于传统搜索引擎,能更为快速准确地找到解决方案。然而传统的推荐往往都是针对广义的程序问题进行推荐,不能挖掘异常信息本身的特点来实现更为针对性的推荐。Stack Overflow与Git Hub是现在流行的软件问答社区以及开源分享社区,包含了大量的软件开发、错误与异常处理的资源。因此本文研究针对软件异常,基于Stack Overflow或Git Hub等在线社区资源进行提取与整合,实现高效、准确的解决方案的智能化推荐,提高软件开发与异常处理的效率,具有良好的研究价值和应用价值。针对以上问题,本文提出了基于Stack Overflow的软件异常抛出问题的在线线索推荐和专家推荐方法。该方法通过分析程序异常特有的关联与结构化信息,构建异常资源树,提出了与异常资源树相关的特征,并对异常相关缺陷错误与专家进行建模;构建支持向量机和学习排序的算法模型,基于大量社区数据进行模型训练,进而通过训练得到的模型实现智能化的推荐。本文的主要贡献包括:(1)通过解析异常堆栈中的关联结构,挖掘异常类型间的继承关系,提出了异常相关资源间的关联算法,构建了异常资源树,将Stack Overflow与Git Hub的跨域资源挂载在同一种体系结构中。(2)提出了文本特征、程序特征和异常资源树特征的三种不同类型的关联特征,进行了有监督的机器学习模型训练,进而实现了针对程序异常错误的在线线索的智能推荐。(3)针对软件异常缺陷错误以及专家进行建模和计算,提出基于兴趣匹配和能力匹配的两种不同类型的关联特征,实现了针对程序异常缺陷修复的Stack Overflow专家的智能推荐。(4)提取并分析大量Stack Overflow与Git Hub在线数据,并进行了丰富的实验对比与分析。实验结果表明,本文的推荐方法在平均检索精度、平均倒数排名以及召回率等指标上相对于现有方法都具有明显的优势。

关键词：推荐系统程序异常缺陷修复 stackoverflow GitHub

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Architectural Knowledge for Technology Decisions in Developer Communities An Exploratory Study with stackoverflow 13

Architectural Knowledge for Technology Decisions in Develope...

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13th Working IEEE/IFIP Conference on Software Architecture (WICSA)

作者： Soliman, Mohamed Galster, Matthias Salama, Amr R. Riebisch, Matthias Univ Hamburg Dept Informat Hamburg Germany Univ Canterbury Christchurch New Zealand

ISBN: (纸本)9781509021314

Architectural decisions have a big influence on basic properties of a software system, and are difficult to change once implemented. Technology decisions deserve special attention because they are one of the most frequently occurring types of architectural decisions. Architectural knowledge is crucial for good decisions. Current architecture knowledge management approaches try to support architects by offering a rich base of architectural solutions and design decision rules. However, they mostly depend on manually capturing and maintaining the architectural knowledge. In this paper, we utilize the most popular online software development community (stackoverflow) as a source of knowledge for technology decisions to support architecture knowledge management approaches with a more efficient methods for knowledge capturing. We conducted an exploratory study, and followed a qualitative and quantitative content analysis approach. We analysed the posts in this community to identify architecture-relevant and technology-related knowledge, and to classify the posts into different types for the purpose of knowledge structuring. In addition, we evaluated our findings through feedback from practitioners.

关键词： architecture design decisions Architecture knowledge empirical study knowledge capturing knowledge management software architecture stackoverflow

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A Journey of Bounty Hunters: Analyzing the Influence of Reward Systems on stackoverflow Question Response Times

A Journey of Bounty Hunters: Analyzing the Influence of Rewa...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Berger, Philipp Hennig, Patrick Bocklisch, Tom Herold, Tom Meinel, Christoph Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9781509044702

Question and Answering (Q&A) platforms are an important source for information and a first place to go when searching for help. Q&A sites, like stackoverflow (SO), use reward systems to incentivize users to answer fast and accurately. In this paper we study and predict the response time for those questions on stackoverflow, that benefit from an additional incentive through so called bounties. Shaped by different motivations and rules these questions perform unlike regular questions. As our key finding we note that topic related factors provide a much stronger evidence than previously found factors for these questions. Finally, we compare models based on these features predicting the response time in the context of bounty questions.

关键词： Bounty Prediction Question answering Response times stackoverflow

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SOLinker: Constructing Semantic Links between Tags and URLs on stackoverflow 40

SOLinker: Constructing Semantic Links between Tags and URLs ...

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40th Annual IEEE Computer Software and Applications Conference Symposium (COMPSAC) / Symposium on Software Engineering Technology and Applications (SETA)

作者： Mo, Wenkai Zhu, Jiangang Qian, Zhenzheng Shen, Beijun Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781467388450

Thanks to the strength of crowdsourcing, there is a lot of useful information on stackoverflow, the most popular Question and Answer (Q&A) platform in software engineering area. This information can be treated as numerous URLs (Uniform Resource Locators), which can be categorized into URLs of Q&As and URLs in Q&As. The domain of former ones is stackoverflow itself, while domains of latter ones are miscellaneous, such as some personal blogs and so on. Although each Q&A has been manually assigned tags, relations between URLs and tags are not clear enough. In this paper, we propose SOLinker, a method to build semantic links between various URLs and tags. Firstly, SOLinker identifies proper relations from a predefined relation set between tags and URLs, which is modeled as a text classification problem. Features are extracted from content of Q&A, the URL and the tag list, and classification algorithms are Logistic Regression and Gradient Boosting Decision Tree, depending on the category of URLs. Secondly, there exists a partial tagging problem, which means for a URL in a Q&A, there are only a part of tags of the Q&A relating to the URL. To address this problem, we propose a semantic analysis method to analyze context of this URL and the URL itself from both implicit and explicit aspects. Then SOLinker will infer proper tags by the label propagation technique. Results show that our method is feasible and practical in constructing semantic links between tags and URLs of/in Q&As. In particular, the F-Score of semantic relation identification is around 78%, 5% higher than the other existing method, and F-Score of partial tagging solving is around 88%.

关键词： Text Classification URL Classification Label Propagation stackoverflow

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Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website - stackoverflow

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JOURNAL OF INFORMATION SCIENCE 2015年第5期41卷 570-583页

作者： Joorabchi, Arash English, Michael Mahdi, Abdulhussain E. Univ Limerick Dept Elect & Comp Engn Limerick Ireland Univ Limerick Dept Comp Sci & Informat Syst Limerick Ireland

The uncontrolled nature of user-assigned tags makes them prone to various inconsistencies caused by spelling variations, synonyms, acronyms and hyponyms. These inconsistencies in turn lead to some of the common problems associated with the use of folksonomies such as the tag explosion phenomenon. Mapping user tags to their corresponding Wikipedia articles, as well-formed concepts, offers multifaceted benefits to the process of subject metadata generation and management in a wide range of online environments. These include normalization of inconsistencies, elimination of personal tags and improvement of the interchangeability of existing subject metadata. In this article, we propose a machine learning-based method capable of automatic mapping of user tags to their equivalent Wikipedia concepts. We have demonstrated the application of the proposed method and evaluated its performance using the currently most popular computer programming Q&A website, ***, as our test platform. Currently, around 20 million posts in stackoverflow are annotated with about 37,000 unique user tags, from which we have chosen a subset of 1256 tags to evaluate the accuracy performance of our proposed mapping method. We have evaluated the performance of our method using the standard information retrieval measures of precision, recall and F-1. Depending on the machine learning-based classification algorithm used as part of the mapping process, F-1 scores as high as 99.6% were achieved.

关键词： Semantic mapping stackoverflow subject metadata user tags Wikipedia

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T-shaped expert mining: a novel approach based on skill translation and focal loss

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2024年第2期62卷 535-554页

作者： Fallahnejad, Zohreh Karimian, Mahmood Lashkari, Fatemeh Beigy, Hamid Sharif Univ Technol Dept Comp Engn Tehran Iran

Hiring knowledgeable and cost-effective individuals, who use their knowledge and expertise to boost the organization, is extremely important for organizations as they are the most valuable assets. T-shaped experts are the best option based on agile methodology. The T-shaped professional has a deep understanding of one topic and broad knowledge of several others. Compared to other types of professionals, T-shaped professionals are better communicators and cheaper to hire. Finding T-shaped experts in a given skill area requires determining each candidate's depth of knowledge and shape of expertise. To estimate each candidate's depth of knowledge in a given skill area, we propose a translation-based method that utilizes two attention-based skill translation models to overcome the vocabulary mismatch between skills and user documents. We also propose two new approaches based on binary cross-entropy and focal loss to determine whether each user is T-shaped. Our experiments on three collections of the stackoverflow dataset demonstrate the efficiency of our proposed method compared to the state-of-the-art approaches.

关键词： Expert Finding T-shaped Experts Attention Mechanism Community Question Answering stackoverflow

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An empirical investigation on the competences and roles of practitioners in Microservices-based Architectures

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 213卷

作者： Ayas, Hamdy Michael Hebig, Regina Leitner, Philipp Chalmers Univ Gothenburg CSE Dept Gothenburg Sweden Univ Rostock Inst Comp Sci Rostock Germany

Microservices-based Architectures (MSAs) are gaining popularity since, among others, they enable rapid and independent delivery of software at scale, facilitating the delivery of business value. Additionally, there are attempts towards understanding practitioners' roles and technical knowledge. MSAs call for affinity in several technologies as well as business domains. This diversity makes it challenging to scope and describe the roles of practitioners. In addition, practitioners often do not receive training and contents of MSA training remain largely undefined, even though there are challenges in finding or developing relevant technical expertise. In this research, we determine the different technical roles that are required in MSAs, along with their detailed competences. We use public online forums (e.g., stackoverflow), where developers share technical knowledge. We analyze 13,517 public profiles of software engineers, deriving their technical competences. Our taxonomy of technical competences in MSAs, contains 11 competences clusters, organized in 3 collections of competences - Web Technologies, DevOps, and Data Technologies. In addition, we derive the roles of microservice practitioners and the characteristics of their roles. Our findings organize the technical competences of MSAs practitioners and determine the training topics and combination of topics that can prepare engineers for MSAs.

关键词： Microservices Technical competences Practitioners profiles Developer roles stackoverflow

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