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Journal articles on the topic 'Mining Software Repositories'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Pinzger, Martin, and Sunghun Kim. "Guest editorial: mining software repositories." Empirical Software Engineering 21, no. 5 (2016): 2033–34. http://dx.doi.org/10.1007/s10664-016-9450-8.

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Robbes, Romain, Yasutaka Kamei, and Martin Pinzger. "Guest Editorial: Mining software repositories." Empirical Software Engineering 22, no. 3 (2017): 1143–45. http://dx.doi.org/10.1007/s10664-017-9527-z.

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3

JUNG, Woosung, Eunjoo LEE, and Chisu WU. "A Survey on Mining Software Repositories." IEICE Transactions on Information and Systems E95.D, no. 5 (2012): 1384–406. http://dx.doi.org/10.1587/transinf.e95.d.1384.

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4

Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to

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5

Nwosu, P. C., F. E. Onuodu, and U. A. Okengwu. "Enhanced Model for Mining Software Repositories." International Journal of Computer Applications 186, no. 40 (2024): 41–46. http://dx.doi.org/10.5120/ijca2024923997.

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6

Kamei, Yasutaka, and Andy Zaidman. "Guest editorial: Mining software repositories 2018." Empirical Software Engineering 25, no. 3 (2020): 2055–57. http://dx.doi.org/10.1007/s10664-020-09817-8.

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Güemes-Peña, Diego, Carlos López-Nozal, Raúl Marticorena-Sánchez, and Jesús Maudes-Raedo. "Emerging topics in mining software repositories." Progress in Artificial Intelligence 7, no. 3 (2018): 237–47. http://dx.doi.org/10.1007/s13748-018-0147-7.

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8

Vandecruys, Olivier, David Martens, Bart Baesens, Christophe Mues, Manu De Backer, and Raf Haesen. "Mining software repositories for comprehensible software fault prediction models." Journal of Systems and Software 81, no. 5 (2008): 823–39. http://dx.doi.org/10.1016/j.jss.2007.07.034.

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9

Zhang, Y., and D. Sheth. "Mining software repositories for model-driven development." IEEE Software 23, no. 1 (2006): 82–90. http://dx.doi.org/10.1109/ms.2006.23.

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10

Otoom, Ahmed Fawzi, Mustafa Hammad, Haneen Hijazi, and Maen Hammad. "Mining expertise of developers from software repositories." International Journal of Computer Applications in Technology 62, no. 3 (2020): 227. http://dx.doi.org/10.1504/ijcat.2020.10028430.

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Hammad, Maen, Haneen Hijazi, Mustafa Hammad, and Ahmed Fawzi Otoom. "Mining expertise of developers from software repositories." International Journal of Computer Applications in Technology 62, no. 3 (2020): 227. http://dx.doi.org/10.1504/ijcat.2020.106581.

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Sun, Xiaobing, Bin Li, Yucong Duan, Wei Shi, and Xiangyue Liu. "Mining Software Repositories for Automatic Interface Recommendation." Scientific Programming 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5475964.

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There are a large number of open source projects in software repositories for developers to reuse. During software development and maintenance, developers can leverage good interfaces in these open source projects and establish the framework of the new project quickly when reusing interfaces in these open source projects. However, if developers want to reuse them, they need to read a lot of code files and learn which interfaces can be reused. To help developers better take advantage of the available interfaces used in software repositories, we previously proposed an approach to automatically r

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13

Reszka, Łukasz, Janusz Sosnowski, and Bartosz Dobrzyński. "Enhancing Software Project Monitoring with Multidimensional Data Repository Mining." Electronics 12, no. 18 (2023): 3774. http://dx.doi.org/10.3390/electronics12183774.

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Software project development and maintenance activities have been reported in various repositories. The data contained in these repositories have been widely used in various studies on specific problems, e.g., predicting bug appearance, allocating issues to developers, and identifying duplicated issues. Developed analysis schemes are usually based on simplified data models while issue report details are neglected. Confronting this problem requires a deep and wide-ranging exploration of software repository contents adapted to their specificities, which differs significantly from classical data

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14

Voinea, Lucian, and Alexandru Telea. "Visual data mining and analysis of software repositories." Computers & Graphics 31, no. 3 (2007): 410–28. http://dx.doi.org/10.1016/j.cag.2007.01.031.

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15

Linstead, Erik, Sushil Bajracharya, Trung Ngo, Paul Rigor, Cristina Lopes, and Pierre Baldi. "Sourcerer: mining and searching internet-scale software repositories." Data Mining and Knowledge Discovery 18, no. 2 (2008): 300–336. http://dx.doi.org/10.1007/s10618-008-0118-x.

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16

Di Penta, Massimiliano, and Tao Xie. "Guest editorial: special section on mining software repositories." Empirical Software Engineering 20, no. 2 (2015): 291–93. http://dx.doi.org/10.1007/s10664-015-9383-7.

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Di Penta, Massimiliano, and Sunghun Kim. "Guest editorial: Special section on mining software repositories." Empirical Software Engineering 21, no. 2 (2016): 301–2. http://dx.doi.org/10.1007/s10664-016-9428-6.

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18

Robbes, Romain, Emily Hill, and Christian Bird. "Guest Editorial: Special section on mining software repositories." Empirical Software Engineering 23, no. 2 (2018): 833–34. http://dx.doi.org/10.1007/s10664-018-9612-y.

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19

Tan, Lin, and Abram Hindle. "Guest Editorial: Special Section on Mining Software Repositories." Empirical Software Engineering 24, no. 3 (2019): 1458–60. http://dx.doi.org/10.1007/s10664-019-09724-7.

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20

Diamantopoulos, Themistoklis, and Andreas L. Symeonidis. "A Directory of Datasets for Mining Software Repositories." Data 10, no. 3 (2025): 28. https://doi.org/10.3390/data10030028.

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The amount of software engineering data is constantly growing, as more and more developers employ online services to store their code, keep track of bugs, or even discuss issues. The data residing in these services can be mined to address different research challenges; therefore, certain initiatives have been established to encourage sharing research datasets collecting them. In this work, we investigate the effect of such an initiative; we create a directory that includes the papers and the corresponding datasets of the data track of the Mining Software Engineering (MSR) conference. Specifica

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21

Himangi. "IMPLEMENTION DATA MINING IN SOFTWARE ENGINEERING." International Journal for Research Publication and Seminar 13, no. 4 (2022): 10. https://doi.org/10.5281/zenodo.7021762.

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<strong>Abstract: </strong>When it comes to software development, software companies generate enormous amounts of data. From the requirements phase all the way through to software maintenance, a new collection of data is generated at every step. Efforts are made to gather and keep data created in software repositories in order to improve the quality of the software. Software repositories include a vast amount of data, which is mined using different Data Mining methods to identify new patterns or highlights in the data. Study in this field has recently been a favourite multidisciplinary researc

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22

Kagdi, Huzefa, and Jonathan I. Maletic. "Mining evolutionary dependencies from web-localization repositories." Journal of Software Maintenance and Evolution: Research and Practice 19, no. 5 (2007): 315–37. http://dx.doi.org/10.1002/smr.355.

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23

Hassan, A. E., A. Mockus, R. C. Holt, and P. M. Johnson. "Guest Editor's Introduction: Special Issue on Mining Software Repositories." IEEE Transactions on Software Engineering 31, no. 6 (2005): 426–28. http://dx.doi.org/10.1109/tse.2005.70.

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24

Bakar, Normi Sham Awang Abu. "Using Language-Based Search in Mining Large Software Repositories." Procedia - Social and Behavioral Sciences 27 (2011): 160–68. http://dx.doi.org/10.1016/j.sbspro.2011.10.594.

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25

Diehl, Stephan, Harald C. Gall, and Ahmed E. Hassan. "Guest editors introduction: special issue on mining software repositories." Empirical Software Engineering 14, no. 3 (2009): 257–61. http://dx.doi.org/10.1007/s10664-009-9110-3.

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26

Xie, Tao, Thomas Zimmermann, and Arie van Deursen. "Introduction to the special issue on mining software repositories." Empirical Software Engineering 18, no. 6 (2013): 1043–46. http://dx.doi.org/10.1007/s10664-013-9273-9.

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27

S., Sharmila Selvi. "STORAGE OPTIMIZATION OF REPOSITORIES USING DATA MINING AND BIG DATA." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 5 (2019): 516–18. https://doi.org/10.5281/zenodo.3234982.

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Software Configuration Management (SCM) deals with various changes and evolution in the software. Each software comprises of thousands of versions. Individual versions need to be stored again and again. Every software keeps on evolving so we need to keep track on each evolution. Software engineer uses mining techniques to store and retrieve these kinds of data’s. This research paper deals with the design, and implementation of an efficient storage management for SCM repositories that facilitates a developer’s to store revisions of software changes using Map reduce Techniques. The m

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28

Canaparo, Marco, and Elisabetta Ronchieri. "Data Mining Techniques for Software Quality Prediction in Open Source Software." EPJ Web of Conferences 214 (2019): 05007. http://dx.doi.org/10.1051/epjconf/201921405007.

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Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve

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29

Hidalgo-Suarez, Carlos Giovanny, Victor Andres Bucheli-Guerrero, and Hugo Armando Ordoñez-Eraso. "VIGHUB: a Technology Forecasting Tool based on Mining Software Repositories." Inge CuC 18, no. 1 (2022): 83–94. http://dx.doi.org/10.17981/ingecuc.18.1.2022.07.

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Introduction: Academics, developers, and companies focused on technological development seek to know what exists and what is still missing in this field. One of the ways they use is the review of bibliographic sources (state-of-the art). In this sense, a tool was developed that allows the current state to be identified semi-automatically. Objective: This article proposes a tool that extracts information from repositories hosted on GitHub. It analyzes the data using computational techniques and presents the results through visualizations that identify the field's technological evolution studied

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30

Hu, Gang, Min Peng, Yihan Zhang, Qianqian Xie, Wang Gao, and Mengting Yuan. "Unsupervised software repositories mining and its application to code search." Software: Practice and Experience 50, no. 3 (2019): 299–322. http://dx.doi.org/10.1002/spe.2760.

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31

Jaafar, Fehmi, Yann-Gaël Guéhéneuc, Sylvie Hamel, and Giuliano Antoniol. "Detecting asynchrony and dephase change patterns by mining software repositories." Journal of Software: Evolution and Process 26, no. 1 (2013): 77–106. http://dx.doi.org/10.1002/smr.1635.

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32

Sun, Xiaobing, Bixin Li, Hareton Leung, Bin Li, and Yun Li. "MSR4SM: Using topic models to effectively mining software repositories for software maintenance tasks." Information and Software Technology 66 (October 2015): 1–12. http://dx.doi.org/10.1016/j.infsof.2015.05.003.

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33

Dobrzyński, Bartosz, and Janusz Sosnowski. "Graph-Driven Exploration of Issue Handling Schemes in Software Projects." Applied Sciences 14, no. 11 (2024): 4723. http://dx.doi.org/10.3390/app14114723.

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The Issue Tracking System (ITS) repositories are rich sources of software development documentation that are useful in assessing the status and quality of software projects. An original model is proposed for tracing issue handling activities and their impact on project progress. As opposed to classical data mining of software repositories, we consider fine-grained features of issues which provide a better insight into project evolution. A thorough analysis of repository contents allows us to define useful metrics for characterizing issue handling schemes. These metrics are derived from the int

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34

Agrawal, Anushree, and R. K. Singh. "Mining Software Repositories for Revision Age-Based Co-Change Probability Prediction." International Journal of Open Source Software and Processes 11, no. 2 (2020): 16–32. http://dx.doi.org/10.4018/ijossp.2020040102.

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Changeability is an important aspect of software maintenance and helps in better planning of development and testing resources. Early detection of change-prone entities is beneficial in terms of both time and money and helps to estimate and meet deadlines reliably. Co-change prediction identifies the affected entities when implementing a change in the software system. Recent researches recommend the use of revision history for the identification of co-changed artifacts. However, very few studies are available for investigation of the effect of history size and age on prediction results. This m

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35

Kaur, Arvinder, and Vidhi Vig. "Mining software repositories for empirical validation of laws of software evolution for Java projects." International Journal of Computational Systems Engineering 2, no. 3 (2016): 155. http://dx.doi.org/10.1504/ijcsyse.2016.079003.

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36

Whitehead, Jim, and Thomas Zimmermann. "Introduction to the Special Issue on Mining Software Repositories in 2010." Empirical Software Engineering 17, no. 4-5 (2012): 500–502. http://dx.doi.org/10.1007/s10664-012-9206-z.

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37

Meqdadi, Omar, Nouh Alhindawi, Jamal Alsakran, Ahmad Saifan, and Hatim Migdadi. "Mining software repositories for adaptive change commits using machine learning techniques." Information and Software Technology 109 (May 2019): 80–91. http://dx.doi.org/10.1016/j.infsof.2019.01.008.

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38

J. Uma, V. Arun Kumar, R. Karthikeyan, V. Lavanya, and P. Priyadharshini. "Integration of Artificial Intelligence into Software Component Reuse: An Overview of Software Intelligence." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 04 (2025): 1086–88. https://doi.org/10.47392/irjaem.2025.0178.

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Artificial Intelligence (AI) is transforming software component reuse by enhancing automation, efficiency, and intelligent retrieval of reusable software artifacts. Traditional reuse methods face challenges in retrieving, classifying, and recommending components due to the complexity of software repositories. AI-driven techniques such as machine learning (ML), natural language processing (NLP), and knowledge graphs help overcome these limitations by enabling intelligent categorization and recommendation. Software Intelligence (SI) enhances reuse by employing data mining techniques to extract p

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39

Williams, C. C., and J. K. Hollingsworth. "Automatic mining of source code repositories to improve bug finding techniques." IEEE Transactions on Software Engineering 31, no. 6 (2005): 466–80. http://dx.doi.org/10.1109/tse.2005.63.

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40

Ali, Nasir, Yann-Gael Gueneuc, and Giuliano Antoniol. "Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links." IEEE Transactions on Software Engineering 39, no. 5 (2013): 725–41. http://dx.doi.org/10.1109/tse.2012.71.

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41

Chen, Tse-Hsun, Stephen W. Thomas, and Ahmed E. Hassan. "A survey on the use of topic models when mining software repositories." Empirical Software Engineering 21, no. 5 (2015): 1843–919. http://dx.doi.org/10.1007/s10664-015-9402-8.

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42

Berciu, Liviu-Marian. "Pydsbuilder – A Dataset Builder Written in Python Django." Studia Universitatis Babeș-Bolyai Informatica 69, no. 2 (2025): 5–22. https://doi.org/10.24193/subbi.2024.2.01.

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Data mining and the analysis of open-source projects have become crucial in recent research, driven by the vast availability of data across multiple programming domains. This paper focuses on two main objectives: first, to present an experience report for designing a software quality data mining tool, and secondly, to provide an open-source solution, PyDs, that facilitates the creation of datasets specifically aimed at analyzing software quality attributes. PyDs, leveraging Python and the Django Framework, provides a comprehensive solution for researchers, encompassing data extraction from rep

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43

M. Ishag, Musa Ibrahim, Hyun Woo Park, Dingkun Li, and Keun Ho Ryu. "Highlighting Current Issues in API Usage Mining to Enhance Software Reusability." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 10 (March 22, 2022): 29–34. http://dx.doi.org/10.37394/232018.2022.10.4.

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The sheer amount of open source codes made available in code repositories and code search engines along with the rapidly increasing releases of Application Programming Interfaces (APIs) have made code devel- opment process easier for programmers. However, learning how to use the elements of an API properly is both challenging and requires learning curve. Mining the available client and test codes can help programmers to iden- tify the best practices in using these APIs. In this paper, we investigate the API usage mining to identify open issues for the researchers. In particular, we make a theo

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44

Nugroho, Yusuf Sulistyo, Hideaki Hata, and Kenichi Matsumoto. "How different are different diff algorithms in Git?" Empirical Software Engineering 25, no. 1 (2019): 790–823. http://dx.doi.org/10.1007/s10664-019-09772-z.

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Abstract Automatic identification of the differences between two versions of a file is a common and basic task in several applications of mining code repositories. Git, a version control system, has a diff utility and users can select algorithms of diff from the default algorithm Myers to the advanced Histogram algorithm. From our systematic mapping, we identified three popular applications of diff in recent studies. On the impact on code churn metrics in 14 Java projects, we obtained different values in 1.7% to 8.2% commits based on the different diff algorithms. Regarding bug-introducing cha

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45

Bouziane, Youcef, Mustapha Kamel Abdi, and Salah Sadou. "Automatically Labelled Software Topic Model." International Journal of Open Source Software and Processes 11, no. 1 (2020): 57–78. http://dx.doi.org/10.4018/ijossp.2020010104.

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Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques. However, this technique does not give an interpretation nor labels to the extracted topics and it requires manual analysis to identify them. Some approaches were proposed to automatically label the topics using tags in SR, but they do not consider the existence of spam-tags and they have difficulties to scale to large tag

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46

Pagano, Dennis, and Walid Maalej. "How Do Developers Blog?" ACM SIGSOFT Software Engineering Notes 46, no. 3 (2021): 26–29. http://dx.doi.org/10.1145/3468744.3468753.

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A decade ago, the rise of GitHub and StackOverflow as social version control and knowledge sharing environments was about to start. Social media like Twitter were mocked by some software engineering researchers and practitioners as "tools for kids not professionals". At that time, we published one of the first papers [12] on social media in software engineering at MSR 2011, the Mining Software Repositories Conference.

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47

Kagdi, Huzefa, Michael L. Collard, and Jonathan I. Maletic. "A survey and taxonomy of approaches for mining software repositories in the context of software evolution." Journal of Software Maintenance and Evolution: Research and Practice 19, no. 2 (2007): 77–131. http://dx.doi.org/10.1002/smr.344.

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48

Alshomali, Mohammad Azees Abdulhassan, and Wurood Albayati. "Mining Github for Factors That Affect Open Source Software Sustainability." International Journal of Membrane Science and Technology 10, no. 4 (2023): 122–33. http://dx.doi.org/10.15379/ijmst.v10i4.1859.

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Open-Source Software (OSS) is everywhere. The availability of such software enables researchers in software engineering to have a deep insight into the factors that affect the success of the software. Some OSS repos get more popular and evolve over time, while others may only survive for a couple of months. This study aims to help developers identify internal factors that affect the sustainability of their software. Firstly, identify the most demanding application domain; secondly, observe the most popular repositories (for the demanded application domain) for about three years to identify the

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49

Vidoni, M. "A systematic process for Mining Software Repositories: Results from a systematic literature review." Information and Software Technology 144 (April 2022): 106791. http://dx.doi.org/10.1016/j.infsof.2021.106791.

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50

Muna, Altherwi. "Assessing Programming Language Impact on Software Development Productivity Based on Mining OSS Repositories." ACM SIGSOFT Software Engineering Notes 44, no. 1 (2019): 36–37. http://dx.doi.org/10.1145/3310013.3310017.

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