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12th Working Conference on Mining Software Repositories (MSR)

作者： Gantayat, Neelamadhav Dhoolia, Pankaj Padhye, Rohan Mani, Senthil Sinha, Vibha Singhal IBM Res Yorktown Hts NY 10598 USA

ISBN: (纸本)9780769555942

stackoverflow's primary goal is to serve as a platform for users to solicit answers regarding programming questions, though its archives are often used by other users who face similar issues and thus it serves a secondary purpose of documenting common problems. The two driving mechanisms for filtering out low quality posts and highlighting the best answers are community votes and the mark of acceptance by the original question asker. But does the asker's choice always match the popular vote? If so, is the asker's choice influenced by the community vote or is the community vote biased towards the accepted answer? And if the asker and community disagree, then can we determine any particular characteristics of posts that influence the choice of the asker and community differently, such as its size, readability, presence of code snippets and external links as well as similarity to the original question? In this paper, we explore the answers to these questions by studying a data-set of all posts on stackoverflow from its launch in September 2008 to September 2014.

关键词： mining software repositories stackoverflow

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The Influence of App Churn on App Success and stackoverflow Discussions 22

The Influence of App Churn on App Success and StackOverflow ...

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22nd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER)

作者： Guerrouj, Latifa Azad, Shams Rigby, Peter C. Concordia Univ Dept Software Engn Montreal PQ Canada

ISBN: (纸本)9781479984695

Gauging the success of software systems has been difficult in the past as there was no uniform measure. With mobile Application (App) Stores, users rate each App according to a common rating scheme. In this paper, we study the impact of App churn on the App success through the analysis of 154 free Android Apps that have a total of 1.2k releases. We provide a novel technique to extract Android API elements used by Apps that developers change between releases. We find that high App churn leads to lower user ratings. For example, we find that on average, per release, poorly rated Apps change 140 methods compared to the 82 methods changed by positively rated Apps. Our findings suggest that developers should not release new features at the expense of churn and user ratings. We also investigate the link between how frequently API classes and methods are changed by App developers relative to the amount of discussion of these code elements on stackoverflow. Our findings indicate that classes and methods that are changed frequently by App developers are in more posts on stackoverflow. We add to the growing consensus that stackoverflow keeps up with the documentation needs of practitioners.

关键词： Android API elements Changes stackoverflow

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Intuition vs. Truth: Evaluation of Common Myths about stackoverflow Posts 12

Intuition vs. Truth: Evaluation of Common Myths about StackO...

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12th Working Conference on Mining Software Repositories (MSR)

作者： Honsel, Verena Herbold, Steffen Grabowski, Jens Univ Gottingen Inst Comp Sci Gottingen Germany

ISBN: (纸本)9780769555942

Posting and answering questions on stackoverflow (SO) is everyday business for many developers. We asked a group of developers what they expect to be true about questions and answers on SO. Most of their expectations were related to the likelihood of getting an answer or to voting behavior. From their comments, we formulated nine myths that they think are true about the platform. Then, we proceeded to use rather simple methods from statistics to check if these myths are supported by the data in the SO dump provided. Through our analysis, we determined that there is an effect for eight of the nine myths the developers believed in. However, for only four of the myths the effect size is large enough to actually make a difference. Hence, we could bust five myths the developers believed in.

关键词： Myths Posting Behavior 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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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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Comparing Foraging Behavior Across Code Hosting and Q&A Platforms through a Gender Lens

Comparing Foraging Behavior Across Code Hosting and Q&A Plat...

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IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

作者： Leon, Shahnewaz Tamanna, Mahzabin Kuttal, Sandeep Kaur NC State Univ Dept Comp Sci Raleigh NC 27695 USA

ISBN: (纸本)9798350329469

This study compares the information foraging behavior of developers on two prominent platforms, stackoverflow and GitHub, which are widely used for code hosting and question and answer purposes. Understanding how developers seek and retrieve information is crucial for designing effective interfaces. In a gender and expertise-balanced study involving 12 developers, we utilized Information Foraging Theory to analyze their foraging behavior. Our findings revealed contrasting patterns, with women spending 30% more time and utilizing 21.24% more cues on GitHub, while men utilized 55% more time and 19.7% more cues on stackoverflow. These insights have significant implications for optimizing website design and information presentation to enhance the efficiency and effectiveness of developers' information-seeking processes.

关键词： Information Foraging Theory GitHub stackoverflow Gender Qualitative Analysis

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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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Modeling Topic-Specific Influential Users in QA Forums Using Association Rule Mining

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IEEE ACCESS 2024年 12卷 196498-196516页

作者： Ishfaq, Umar Ali, Tassawar Daud, Ali Alreshoodi, Mohammed Irshad, Azeem COMSATS Univ Islamabad Dept Comp Sci Wah Campus Wah Cantt 470040 Pakistan Rabdan Acad Fac Resilience Abu Dhabi U Arab Emirates Qassim Univ Appl Coll Unit Sci Res Buraydah 52571 Saudi Arabia GGC Asghar Mall Punjab Higher Educ Dept Fac Comp Sci Rawalpindi 46040 Pakistan

Social networks have enabled the exchange of information that has accelerated the diffusion of online content. Social networks offer multiple forms of social interaction that facilitate users in generating online content. The increasing popularity of social networks has attracted many researchers to investigate their impact on users and confirmed significant influence of social networks on markets, social life, and politics. Therefore, identification of the influential users in social networks has become a popular research area due to several of its applications in diverse domains such as e-commerce, viral marketing, political campaigns etc. However, earlier research either consider linguistic content or utilize network-based representation for finding the influential users in social networks. This paper incorporates association rule mining (ARM) based algorithms, that are mostly used for market-basket analysis, for exploring the behavior of users and predict their participation in social interactions. The study identifies topic-based influential users using Apriori and FPGrowth on the dataset of a popular online question-answer community. In addition, the study employs standard evaluation metrics such as confidence, support, lift, and conviction for computing association rules. Subsequently, the obtained results are validated using conventional measures including accuracy, precision, and recall in accordance with the perspective of association rules. The experiments are performed on multiple social networking datasets and the obtained results prove the visibility and quality of the proposed method against well-established degree centrality, PageRank, and HITS. In addition, the results validate the effectiveness of ARM in identifying influential users in social networks. The generated rule set can help create efficient decision support systems that are becoming prevalent in providing solutions to real-world problems.

关键词： Social networking (online) Association rule learning Message systems Market research Prediction algorithms Patents Clustering algorithms Itemsets Text mining Technological innovation classification influential users QA forum social network analysis stackoverflow deep learning

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EPAN-SERec: Expertise preference-aware networks for software expert recommendations with knowledge graph

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 244卷

作者： Tang, Mingjing Wu, Di Zhang, Shu 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

The software knowledge community provides software developers with valuable knowledge of technologies, activities, tools and project management related to software development. However, a large number of unresolved questions and the lack of expert participation have become evident and critical challenges for the software knowledge community. To address the problems of label dependence, interactive data's sparsity and unassociated knowledge in community-based software expert recommendation, we propose an Expertise PreferenceAware Network model for Software Expert Recommendation (EPAN-SERec) with knowledge graph. Firstly, the software knowledge graph is utilized as an auxiliary resource to provide domain knowledge representation. Secondly, we devise an expertise preference-learning framework by means of deep reinforcement learning that models the historical interactive information of experts and generate the expertise preference weight graph. To better learn expertise preference features, a graph convolutional network (GCN) model with integrated graph self-supervised learning is proposed to optimize the features representation. Finally, software knowledge entity embeddings with semantic information are obtained by exploiting the graph-embedding model, and the final features of question to be answered are obtained by fusing the expertise preference of experts. Extensive experiments on the dataset based on stackoverflow demonstrate that our approach achieves a better outcome than baseline models.

关键词： Expert recommendation Knowledge graph Deep reinforcement learning Graph self-supervised learning stackoverflow

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