Mining duplicate questions in stack overflow

Ahasanuzzaman, Muhammad; Asaduzzaman, Muhammad; Roy, Chanchal K.; Schneider, Kevin A.

doi:10.1145/2901739.2901770

Cited by 99 publications

(110 citation statements)

References 28 publications

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“…Complementary, Zhang et al analyzed the developed features of the prior work and suggest that combining vector similarity, relevance and association features gives the best performance for Stack Overflow duplicate detection [37]. This paper is based on two studies: one study published in 2015 by Zhang et al [14] and another study published in 2016 by Ahasanuzzaman et al [15]. The next sections present the reproduction of those works, and describing the adopted study process, study design, and study execution.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, we describe the common ground to evaluate and reproduce DupPredictor [14] and Dupe [15]. All the experiments were conducted over a server equipped with Intel® Xeon® at 2.4 GHz on 80 GB RAM, twelve cores, and 64-bit Linux Mint Cinnamon operating system.…”

Section: Preliminariesmentioning

confidence: 99%

“…We could, at first, use the MSR'15 [22] data set in our replicates to reproduce both works since it is suppose to contain the ground truth of DupPredictor [14] and Dupe [15], this way not relying on MSR'13 data set [21] where duplicates were manually identified. However, in order to extend the results to more recent years, we would need more data sets containing more recent posts.…”

Section: A Data Setmentioning

confidence: 99%

“…Besides incentives, there exist automated solutions to detect duplicates. But, we keep seeing excessive duplicates cluttering up over time [15]. For instance, Figure 1 shows two questions from Stack Overflow, where question 1(a) was appointed by the community as the duplicate of question 1(b).…”

Section: Introductionmentioning

confidence: 99%

“…In particular, Zhang et al [14] envisioned DupPredictor, an approach to predict whether a question is a duplicate; Then, Ahasanuzzaman et al [15] propose Dupe, which also extracted features from the question corpus to build a question pair binary classifier (duplicated or not).…”

Section: Introductionmentioning

confidence: 99%

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Duplicate Question Detection in Stack Overflow: A Reproducibility Study

Silva

Paixão

Maia

2018

Preprint

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Abstract-Stack Overflow has become a fundamental element of developer toolset. Such influence increase has been accompanied by an effort from Stack Overflow community to keep the quality of its content. One of the problems which jeopardizes that quality is the continuous growth of duplicated questions. To solve this problem, prior works focused on automatically detecting duplicated questions. Two important solutions are DupPredictor and Dupe. Despite reporting significant results, both works do not provide their implementations publicly available, hindering subsequent works in scientific literature which rely on them. We executed an empirical study as a reproduction of DupPredictor and Dupe. Our results, not robust when attempted with different set of tools and data sets, show that the barriers to reproduce these approaches are high. Furthermore, when applied to more recent data, we observe a performance decay of our both reproductions in terms of recall-rate over time, as the number of questions increases. Our findings suggest that the subsequent works concerning detection of duplicated questions in Question and Answer communities require more investigation to assert their findings.

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Section: Related Workmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: A Data Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Duplicate Question Detection in Stack Overflow: A Reproducibility Study

Silva

Paixão

Maia

2018

Preprint

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Mining software architecture knowledge: Classifying stack overflow posts using machine learning

Ali

Mushtaq

Rasheed

et al. 2021

Concurrency and Computation

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Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions.The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning-based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K-Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni-gram feature set provides best classification results and attains 85.80% accuracy.For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni-gram feature set proved to be the most suitable. K E Y W O R D Sarchitectural knowledge management, stack overflow, crowd-sourced communities, text mining, classification INTRODUCTIONSoftware architectural design appears to have a significant importance in Software Development Life Cycle (SDLC). Its manifests are the early design decisions which help to determine the system development, deployment, and evaluation. To develop a quality project, better architectural decisions are essential and these decisions pose vast challenges for software engineers. Architectural Knowledge Management (AKM) is used to capture

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Richen: Automated enrichment of Git documentation with usage examples and scenarios

Shen,

Yang,

Jia

et al. 2024

J Software Evolu Process

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As the predominant modern version control system, Git has become an indispensable tool for both commercial and open‐source software projects. It substantially improves software development effectiveness and efficiency through its distributed version control system, fostering seamless collaboration among teams and across locations. However, research has found that many developers have doubts about using Git commands, while the official Git documentation is rather scanty, that is, lacking sufficient explanations and examples. To help developers learn and use Git commands, we propose the first approach (Richen) for enriching Git documentation with usage examples and scenarios by leveraging crowd knowledge from Stack Overflow. Richen retrieves Git‐related posts from Stack Overflow, extracts relevant Q&A pairs, and selects representative command usages, including usage examples and scenarios, for different Git commands. Experimental results have shown that Richen can extract informative and concise command usages for Git commands. Compared with alternative methods adapted from API usage mining, the command usages obtained by Richen have significant advantages in terms of relevance, readability, and usability. Furthermore, we have shown through an empirical study that the command usages extracted by Richen can better help developers complete Git command‐related tasks.

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Mining duplicate questions in stack overflow

Cited by 99 publications

References 28 publications

Duplicate Question Detection in Stack Overflow: A Reproducibility Study

Duplicate Question Detection in Stack Overflow: A Reproducibility Study

Mining software architecture knowledge: Classifying stack overflow posts using machine learning

Richen: Automated enrichment of Git documentation with usage examples and scenarios

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