检索结果-内蒙古大学图书馆

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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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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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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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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On the Effectiveness of Transfer Learning for Code Search

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第4期49卷 1804-1822页

作者： Salza, Pasquale Schwizer, Christoph Gu, Jian Gall, Harald C. Univ Zurich Zurich Switzerland

The Transformer architecture and transfer learning have marked a quantum leap in natural language processing, improving the state of the art across a range of text-based tasks. This paper examines how these advancements can be applied to and improve code search. To this end, we pre-train a BERT-based model on combinations of natural language and source code data and fine-tune it on pairs of stackoverflow question titles and code answers. Our results show that the pre-trained models consistently outperform the models that were not pre-trained. In cases where the model was pre-trained on natural language "and" source code data, it also outperforms an information retrieval baseline based on Lucene. Also, we demonstrated that the combined use of an information retrieval-based approach followed by a Transformer leads to the best results overall, especially when searching into a large search pool. Transfer learning is particularly effective when much pre-training data is available and fine-tuning data is limited. We demonstrate that natural language processing models based on the Transformer architecture can be directly applied to source code analysis tasks, such as code search. With the development of Transformer models designed more specifically for dealing with source code data, we believe the results of source code analysis tasks can be further improved.

关键词： Codes Bit error rate Task analysis Transformers Micromechanical devices Transfer learning Data models Code search transfer learning source code modeling multimodal embeddings stackoverflow deep learning

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What is asked about technical debt (TD) on Stack Exchange question-and-answer (Q&A) websites? An observational study

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EMPIRICAL SOFTWARE ENGINEERING 2023年第2期28卷 1-27页

作者： Alfayez, Reem Ding, Yunyan Winn, Robert Alfayez, Ghaida Harman, Christopher Boehm, Barry King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Univ Southern Calif Ctr Syst & Software Engn Los Angeles CA USA

Technical debt (TD) is a term coined by agile software pioneer Ward Cunningham to account for the added software system effort or cost resulting from taking early software project shortcuts. Previous research on TD has extensively outlined and discussed the various consequences derived from accumulating TD and the difficulty in managing it. A review of the software engineering literature revealed that Stack Exchange question-and-answer (Q&A) websites can provide valuable, real world perspectives on a number of software engineering topics. Therefore, this study aims to observe how the TD term is utilized on Stack Exchange Q&A websites. Specifically, this study utilizes a dataset derived from three Stack Exchange Q&A websites, which are Stack Overflow (SO), Software Engineering (SE), and Project Management (PM), to retrieve and analyze 578 TD-related questions. The results unveiled that TD-related questions can be categorized into 14 different categories, a total of 636 unique tags are utilized in the acquired set of TD-related questions, and a few TD-related categories both lack accepted answers and have a longer median time to receive an accepted answer than other categories. This study's findings highlight the TD-related challenges that are addressed by Stack Exchange Q&A website users, which may prove beneficial in steering future TD-related efforts.

关键词： Technical debt Technical debt management Software engineering Question-and-answer (Q&A) websites 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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SCL-SKG: software knowledge extraction with span-level contrastive learning

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第SUPPL 2期56卷 2383-2406页

作者： Tang, Mingjing Zhang, Shu Zheng, Ming Ma, Zifei Gao, Wei Yunnan Normal Univ Key Lab Educ Informatizat Nationalities Minist Educ Kunming Peoples R China Yunnan Normal Univ Yunnan Key Lab Smart Educ Kunming Peoples R China Yunnan Normal Univ Sch Informat Sci & Technol Kunming Peoples R China Anhui Normal Univ Sch Comp & Informat Wuhu Peoples R China Yunnan Agr Univ Sch Water Conservancy Kunming Peoples R China

The text of software knowledge community contains abundant knowledge of software engineering field. The software knowledge entity and relation can be extracted automatically and efficiently to form the software knowledge graph, which is helpful for software knowledge-centric intelligent applications, such as intelligent question answering, automatic document generation and software expert recommendation. Most existing methods are confronted with problems of task dependence and entity overlap. In this paper, we propose a software knowledge extraction method based on span-level contrastive learning. From the level of sentence sequence modelling, we model the sentence sequence with span as a unit, and generate abundant positive and negative samples of entity span through the span representation layer to avoid the problem that the token-level method cannot select overlapping entities. From the level of feature learning, we propose supervised entity contrastive learning and relation contrastive learning, which obtain enhanced feature representation of entity span and entity pair through positive and negative sample enhancement and contrastive loss function construction. Experiments are conducted on the dataset which is constructed based on texts of the stackoverflow, and show that our approach achieves a better performance than baseline models.

关键词： Software knowledge graph Entity extraction Relation extraction Contrastive learning stackoverflow

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Augmenting and structuring user queries to support efficient free-form code search

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EMPIRICAL SOFTWARE ENGINEERING 2018年第5期23卷 2622-2654页

作者： Sirres, Raphael Bissyand, Tegawende F. Kim, Dongsun Lo, David Klein, Jacques Kim, Kisub Le Traon, Yves Natl Lib Luxembourg 37 Blvd FD L-2450 Roosevelt Luxembourg Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust 29 Ave JF L-1855 Kennedy Luxembourg Singapore Management Univ Sch Informat Syst 80 Stamford Rd Singapore 178902 Singapore

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code search engine, on top of GitHub and Stack Overflow Q&A data. We evaluate GitSearch in several dimensions to demonstrate that (1) its code search results are correct with respect to user-accepted answers;(2) the results are qualitatively better than those of existing Internet-scale code search engines;(3) our engine is competitive against web search engines, such as Google, in helping users solve programming tasks;and (4) GitSearch provides code examples that are acceptable or interesting to the community as answers for Stack Overflow questions.

关键词： Code search GitHub Free-form search Query augmentation stackoverflow Vocabulary mismatch

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Developer Identity Linkage and Behavior Mining Across GitHub and StackOveflow

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2017年第9-10期27卷 1409-1425页

作者： Xiong, Yunxiang Meng, Zhangyuan Shen, Beijun Yin, Wei Shanghai Jiao Tong Univ Sch Software Shanghai 200240 Peoples R China China Aeronaut Radio Elect Res Inst Shanghai 200233 Peoples R China

Nowadays, software developers are increasingly involved in GitHub and stackoverflow, creating a lot of valuable data in the two communities. Researchers mine the information in these software communities to understand developer behaviors, while previous works mainly focus on mining data within a single community. In this paper, we propose a novel approach to developer identity linkage and behavior mining across GitHub and stackoverflow. This approach links the accounts from two communities using a CART decision tree, leveraging the features from usernames, user behaviors and writing styles. Then, it explores cross-site developer behaviors through T-graph analysis, LDA-based topics clustering and cross-site tagging. We conducted several experiments to evaluate this approach. The results show that the precision and F-score of our identity linkage method are higher than previous methods in software communities. Especially, we discovered that (1) active issue committers are also active question askers;(2) for most developers, the topics of their contents in GitHub are similar to those of those questions and answers in stackoverflow;(3) developers' concerns in stackoverflow shift over the time of their current participating projects in GitHub;(4) developers' concerns in GitHub are more relevant to their answers than questions and comments in stackoverflow.

关键词： Identity linkage developer behavior mining GitHub stackoverflow

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