Relevant bibliographies by topics / Mining Source Code Repositories

Academic literature on the topic 'Mining Source Code Repositories'

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Published: 11 March 2023

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Journal articles on the topic "Mining Source Code Repositories"

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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 (June 2005): 466–80. http://dx.doi.org/10.1109/tse.2005.63.

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Kagdi, Huzefa, Michael L. Collard, and Jonathan I. Maletic. "Towards a taxonomy of approaches for mining of source code repositories." ACM SIGSOFT Software Engineering Notes 30, no. 4 (July 2005): 1–5. http://dx.doi.org/10.1145/1082983.1083159.

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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 theoretical comparison of the API usage pattern mining and highlight unresolved issues along with proper suggestions to address them.
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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 recommend interfaces by mining existing open source projects in the software repositories. We mainly used the LDA (Latent Dirichlet Allocation) topic model to construct the Feature-Interface Graph for each software project and recommended the interfaces based on the Feature-Interface Graph. In this paper, we improve our previous approach by clustering the recommending interfaces on the Feature-Interface Graph, which can recommend more accurate interfaces for developers to reuse. We evaluate the effectiveness of the improved approach and the results show that the improved approach can be more efficient to recommend more accurate interfaces for reuse over our previous work.
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Pinzger, Martin, Emanuel Giger, and Harald C. Gall. "Comparing fine-grained source code changes and code churn for bug prediction - A retrospective." ACM SIGSOFT Software Engineering Notes 46, no. 3 (July 14, 2021): 21–23. http://dx.doi.org/10.1145/3468744.3468751.

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More than two decades ago, researchers started to mine the data stored in software repositories to help software developers in making informed decisions for developing and testing software systems. Bug prediction was one of the most promising and popular research directions that uses the data stored in software repositories to predict the bug-proneness or number of bugs in source files. On that topic and as part of Emanuel's PhD studies, we submitted a paper with the title Comparing fine-grained source code changes and code churn for bug prediction [8] to the 8th Working Conference on Mining Software Engineering, held 2011 in beautiful Honolulu, Hawaii. Ten years later, it got selected as one of the finalists to receive the MSR 2021 Most Influential Paper Award. In the following, we provide a retrospective on our work, describing the road to publishing this paper, its impact in the field of bug prediction, and the road ahead.
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Nugroho, Yusuf Sulistyo, Hideaki Hata, and Kenichi Matsumoto. "How different are different diff algorithms in Git?" Empirical Software Engineering 25, no. 1 (September 11, 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 change identification, we found 6.0% and 13.3% in the identified bug-fix commits had different results of bug-introducing changes from 10 Java projects. For patch application, we found that the Histogram is more suitable than Myers for providing the changes of code, from our manual analysis. Thus, we strongly recommend using the Histogram algorithm when mining Git repositories to consider differences in source code.
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SCOTTO, MARCO, ALBERTO SILLITTI, and GIANCARLO SUCCI. "AN EMPIRICAL ANALYSIS OF THE OPEN SOURCE DEVELOPMENT PROCESS BASED ON MINING OF SOURCE CODE REPOSITORIES." International Journal of Software Engineering and Knowledge Engineering 17, no. 02 (April 2007): 231–47. http://dx.doi.org/10.1142/s0218194007003215.

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This paper presents an empirical analysis of the Open Source development process from the point of view of the involvement of the developers in the production process. The study focuses on how developers contribute to projects in terms of involvement, size and kind of their contribution. Data have been collected from 53 Open Source projects and target application domains include different areas: web and application servers, databases, operating systems, and window managers. Collected data include the number of developers, patterns of code modifications, and evolution over the time of size and complexity. The results of this study show evidence that there are recurrent patterns in Open Source software development and these patterns are common to all the projects considered even if there are no superimposed processes for development, application domains are different, and there are contributions from people spread across the world.
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Saini, Munish, Sandeep Mehmi, and Kuljit Kaur Chahal. "Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data." Advances in Fuzzy Systems 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1479692.

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Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future.
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Schreiber, Roland Robert. "Organizational Influencers in Open-Source Software Projects." International Journal of Open Source Software and Processes 14, no. 1 (February 16, 2023): 1–20. http://dx.doi.org/10.4018/ijossp.318400.

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Traditional software development is shifting toward the open-source development model, particularly in the current environment of competitive challenges to develop software openly. The author employs a case study approach to investigate how organizations and their affiliated developers collaborate in the open-source software (OSS) ecosystem TensorFlow (TF). The analysis of the artificial intelligence OSS library TF combines social network analysis (SNA) and an examination of archival data by mining software repositories. The study looks at the structure and evolution of code-collaboration among developers and with the ecosystem's organizational networks over the TF lifespan. These involved organizations play a particularly critical role in development. The research also looks at productivity, homophily, development, and diversity among developers. The results deepen the understanding of OSS communities' collaborative developer and organization patterns. Furthermore, the study emphasizes the importance and evolution of social networks, diversity, and productivity in ecosystems.
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Lu, Mingming, Yan Liu, Haifeng Li, Dingwu Tan, Xiaoxian He, Wenjie Bi, and Wendbo Li. "Hyperbolic Function Embedding: Learning Hierarchical Representation for Functions of Source Code in Hyperbolic Space." Symmetry 11, no. 2 (February 18, 2019): 254. http://dx.doi.org/10.3390/sym11020254.

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Recently, source code mining has received increasing attention due to the rapid increase of open-sourced code repositories and the tremendous values implied in this large dataset, which can help us understand the organization of functions or classes in different software and analyze the impact of these organized patterns on the software behaviors. Hence, learning an effective representation model for the functions of source code, from a modern view, is a crucial problem. Considering the inherent hierarchy of functions, we propose a novel hyperbolic function embedding (HFE) method, which can learn a distributed and hierarchical representation for each function via the Poincaré ball model. To achieve this, a function call graph (FCG) is first constructed to model the call relationship among functions. To verify the underlying geometry of FCG, the Ricci curvature model is used. Finally, an HFE model is built to learn the representations that can capture the latent hierarchy of functions in the hyperbolic space, instead of the Euclidean space, which are usually used in those state-of-the-art methods. Moreover, HFE is more compact in terms of lower dimensionality than the existing graph embedding methods. Thus, HFE is more effective in terms of computation and storage. To experimentally evaluate the performance of HFE, two application scenarios, namely, function classification and link prediction, have been applied. HFE achieves up to 7.6% performance improvement compared to the chosen state-of-the-art methods, namely, Node2vec and Struc2vec.
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Dissertations / Theses on the topic "Mining Source Code Repositories"

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Kagdi, Huzefa H. "Mining Software Repositories to Support Software Evolution." Kent State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=kent1216149768.

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Bengtsson, Jonathan, and Heidi Hokka. "Analysing Lambda Usage in the C++ Open Source Community." Thesis, Mittuniversitetet, Institutionen för data- och systemvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39514.

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Object-oriented languages have made a shift towards incorporating functional concepts such as lambdas. Lambdas are anonymous functions that can be used within the scope of other functions. In C++ lambdas are considered difficult to use for inexperienced developers. This implies that there may be problems with lambdas in C++. However, studies about lambdas in C++ repositories are scarce, compared to other object-oriented languages such as Java. This study aims to address a knowledge gap regarding how lambdas are used by developers in C++ repositories. Furthermore, examine how developer experience and software engineering practices, such as unit testing and in-code documentation, correlates with the inclusion of lambdas. To achieve this we create a set of tools that statically analyse repositories to gather results. This study gained insight into the number of repositories utilising lambdas, their usage areas, and documentation but also how these findings compare to similar studies’ results in Java. Further, it is shown that unit testing and developer experience correlates with the usage of lambdas.
Objektorienterade språk har gjort en förskjutning mot att integrera funktionella begrepp som lambdas. Lambdas är anonyma funktioner som kan användas inom ramen för andra funktioner. I C ++ anses lambdas vara svåra att använda för oerfarna utvecklare. Detta innebär att det kan vara problem med lambdas i C ++. Emellertid är studier på lambdas i C ++ repositorier mindre vanliga jämfört med andra objektorienterade språk som Java. Denna studie syftar till att ta itu med ett kunskapsgap beträffande hur lambdas används av utvecklare i C++ repositorier. Dessutom undersöks hur utvecklarvanor och sedvänjor i programvaruutveckling, till exempel enhetstestning och dokumentation, korrelerar med inkluderingen av lambdas. För att uppnå detta skapar vi en uppsättning verktyg som statiskt analyserar repositorier för att samla resultat. Denna studie fick inblick i antalet repositorier som använder lambdas, deras användningsområden och dokumentation men också hur dessa resultat jämför sig med liknande studieresultat i Java. Vidare har det visats att enhetstestning och utvecklaren erfarenhet korrelerar med användningen av lambdas.
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Schulte, Lukas. "Investigating topic modeling techniques for historical feature location." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-85379.

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Software maintenance and the understanding of where in the source code features are implemented are two strongly coupled tasks that make up a large portion of the effort spent on developing applications. The concept of feature location investigated in this thesis can serve as a supporting factor in those tasks as it facilitates the automation of otherwise manual searches for source code artifacts. Challenges in this subject area include the aggregation and composition of a training corpus from historical codebase data for models as well as the integration and optimization of qualified topic modeling techniques. Building up on previous research, this thesis provides a comparison of two different techniques and introduces a toolkit that can be used to reproduce and extend on the results discussed. Specifically, in this thesis a changeset-based approach to feature location is pursued and applied to a large open-source Java project. The project is used to optimize and evaluate the performance of Latent Dirichlet Allocation models and Pachinko Allocation models, as well as to compare the accuracy of the two models with each other. As discussed at the end of the thesis, the results do not indicate a clear favorite between the models. Instead, the outcome of the comparison depends on the metric and viewpoint from which it is assessed.
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Vu, Duc Ly. "Towards Understanding and Securing the OSS Supply Chain." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/333508.

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Free and Open-Source Software (FOSS) has become an integral part of the software supply chain in the past decade. Various entities (automated tools and humans) are involved at different stages of the software supply chain. Some actions that occur in the chain may result in vulnerabilities or malicious code injected in a published artifact distributed in a package repository. At the end of the software supply chain, developers or end-users may consume the resulting artifacts altered in transit, including benign and malicious injection. This dissertation starts from the first link in the software supply chain, ‘developers’. Since many developers do not update their vulnerable software libraries, thus exposing the user of their code to security risks. To understand how they choose, manage and update the libraries, packages, and other Open-Source Software (OSS) that become the building blocks of companies’ completed products consumed by end-users, twenty-five semi-structured interviews were conducted with developers of both large and small-medium enterprises in nine countries. All interviews were transcribed, coded, and analyzed according to applied thematic analysis. Although there are many observations about developers’ attitudes on selecting dependencies for their projects, additional quantitative work is needed to validate whether behavior matches or whether there is a gap. Therefore, we provide an extensive empirical analysis of twelve quality and popularity factors that should explain the corresponding popularity (adoption) of PyPI packages was conducted using our tool called py2src. At the end of the software supply chain, software libraries (or packages) are usually downloaded directly from the package registries via package dependency management systems under the comfortable assumption that no discrepancies are introduced in the last mile between the source code and their respective packages. However, such discrepancies might be introduced by manual or automated build tools (e.g., metadata, Python bytecode files) or for evil purposes (malicious code injects). To identify differences between the published Python packages in PyPI and the source code stored on Github, we developed a new approach called LastPyMile . Our approach has been shown to be promising to integrate within the current package dependency management systems or company workflow for vetting packages at a minimal cost. With the ever-increasing numbers of software bugs and security vulnerabilities, the burden of secure software supply chain management on developers and project owners increases. Although automated program repair approaches promise to reduce the burden of bug-fixing tasks by suggesting likely correct patches for software bugs, little is known about the practical aspects of using APR tools, such as how long one should wait for a tool to generate a bug fix. To provide a realistic evaluation of five state-of-the-art APR tools, 221 bugs from 44 open-source Java projects were run within a reasonable developers’ time and effort.
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Carlsson, Emil. "Mining Git Repositories : An introduction to repository mining." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-27742.

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When performing an analysis of the evolution of software quality and software metrics,there is a need to get access to as many versions of the source code as possible. There isa lack of research on how data or source code can be extracted from the source controlmanagement system Git. This thesis explores different possibilities to resolve thisproblem. Lately, there has been a boom in usage of the version control system Git. Githubalone hosts about 6,100,000 projects. Some well known projects and organizations thatuse Git are Linux, WordPress, and Facebook. Even with these figures and clients, thereare very few tools able to perform data extraction from Git repositories. A pre-studyshowed that there is a lack of standardization on how to share mining results, and themethods used to obtain them. There are several tools available for older version control systems, such as concurrentversions system (CVS), but few for Git. The examined repository mining applicationsfor Git are either poorly documented; or were built to be very purpose-specific to theproject for which they were designed. This thesis compiles a list of general issues encountered when using repositorymining as a tool for data gathering. A selection of existing repository mining tools wereevaluated towards a set of prerequisite criteria. The end result of this evaluation is thecreation of a new repository mining tool called Doris. This tool also includes a smallcode metrics analysis library to show how it can be extended.
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Sinha, Vinayak. "Sentiment Analysis On Java Source Code In Large Software Repositories." Youngstown State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1464880227.

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Ribeiro, Athos Coimbra. "Ranking source code static analysis warnings for continuous monitoring of free/libre/open source software repositories." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-20082018-170140/.

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While there is a wide variety of both open source and proprietary source code static analyzers available in the market, each of them usually performs better in a small set of problems, making it hard to choose one single tool to rely on when examining a program. Combining the analysis of different tools may reduce the number of false negatives, but yields a corresponding increase in the number of false positives (which is already high for many tools). An interesting solution, then, is to filter these results to identify the issues least likely to be false positives. This work presents kiskadee, a system to support the usage of static analysis during software development by providing carefully ranked static analysis reports. First, it runs multiple static analyzers on the source code. Then, using a classification model, the potential bugs detected by the static analyzers are ranked based on their importance, with critical flaws ranked first, and potential false positives ranked last. To train kiskadee\'s classification model, we post-analyze the reports generated by three tools on synthetic test cases provided by the US National Institute of Standards and Technology. To make our technique as general as possible, we limit our data to the reports themselves, excluding other information such as change histories or code metrics. The features extracted from these reports are used to train a set of decision trees using AdaBoost to create a stronger classifier, achieving 0.8 classification accuracy (the combined false positive rate from the used tools was 0.61). Finally, we use this classifier to rank static analyzer alarms based on the probability of a given alarm being an actual bug. Our experimental results show that, on average, when inspecting warnings ranked by kiskadee, one hits 5.2 times less false positives before each bug than when using a randomly sorted warning list.
Embora exista grande variedade de analisadores estáticos de código-fonte disponíveis no mercado, tanto com licenças proprietárias, quanto com licenças livres, cada uma dessas ferramentas mostra melhor desempenho em um pequeno conjunto de problemas distinto, dificultando a escolha de uma única ferramenta de análise estática para analisar um programa. A combinação das análises de diferentes ferramentas pode reduzir o número de falsos negativos, mas gera um aumento no número de falsos positivos (que já é alto para muitas dessas ferramentas). Uma solução interessante é filtrar esses resultados para identificar os problemas com menores probabilidades de serem falsos positivos. Este trabalho apresenta kiskadee, um sistema para promover o uso da análise estática de código fonte durante o ciclo de desenvolvimento de software provendo relatórios de análise estática ranqueados. Primeiramente, kiskadee roda diversos analisadores estáticos no código-fonte. Em seguida, utilizando um modelo de classificação, os potenciais bugs detectados pelos analisadores estáticos são ranqueados conforme sua importância, onde defeitos críticos são colocados no topo de uma lista, e potenciais falsos positivos, ao fim da mesma lista. Para treinar o modelo de classificação do kiskadee, realizamos uma pós-análise nos relatórios gerados por três analisadores estáticos ao analisarem casos de teste sintéticos disponibilizados pelo National Institute of Standards and Technology (NIST) dos Estados Unidos. Para tornar a técnica apresentada o mais genérica possível, limitamos nossos dados às informações contidas nos relatórios de análise estática das três ferramentas, não utilizando outras informações, como históricos de mudança ou métricas extraídas do código-fonte dos programas inspecionados. As características extraídas desses relatórios foram utilizadas para treinar um conjunto de árvores de decisão utilizando o algoritmo AdaBoost para gerar um classificador mais forte, atingindo uma acurácia de classificação de 0,8 (a taxa de falsos positivos das ferramentas utilizadas foi de 0,61, quando combinadas). Finalmente, utilizamos esse classificador para ranquear os alarmes dos analisadores estáticos nos baseando na probabilidade de um dado alarme ser de fato um bug no código-fonte. Resultados experimentais mostram que, em média, quando inspecionando alarmes ranqueados pelo kiskadee, encontram-se 5,2 vezes menos falsos positivos antes de se encontrar cada bug quando a mesma inspeção é realizada para uma lista ordenada de forma aleatória.
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Hassan, Ahmed. "Mining Software Repositories to Assist Developers and Support Managers." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1017.

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This thesis explores mining the evolutionary history of a software system to support software developers and managers in their endeavors to build and maintain complex software systems. We introduce the idea of evolutionary extractors which are specialized extractors that can recover the history of software projects from software repositories, such as source control systems. The challenges faced in building C-REX, an evolutionary extractor for the C programming language, are discussed. We examine the use of source control systems in industry and the quality of the recovered C-REX data through a survey of several software practitioners. Using the data recovered by C-REX, we develop several approaches and techniques to assist developers and managers in their activities. We propose Source Sticky Notes to assist developers in understanding legacy software systems by attaching historical information to the dependency graph. We present the Development Replay approach to estimate the benefits of adopting new software maintenance tools by reenacting the development history. We propose the Top Ten List which assists managers in allocating testing resources to the subsystems that are most susceptible to have faults. To assist managers in improving the quality of their projects, we present a complexity metric which quantifies the complexity of the changes to the code instead of quantifying the complexity of the source code itself. All presented approaches are validated empirically using data from several large open source systems. The presented work highlights the benefits of transforming software repositories from static record keeping repositories to active repositories used by researchers to gain empirically based understanding of software development, and by software practitioners to predict, plan and understand various aspects of their project.
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Thummalapenta, Suresh. "Improving Software Productivity and Quality via Mining Source Code." NORTH CAROLINA STATE UNIVERSITY, 2011. http://pqdtopen.proquest.com/#viewpdf?dispub=3442531.

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Delorey, Daniel Pierce. "Observational Studies of Software Engineering Using Data from Software Repositories." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1716.pdf.

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Book chapters on the topic "Mining Source Code Repositories"

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Scheidgen, Markus, and Joachim Fischer. "Model-Based Mining of Source Code Repositories." In System Analysis and Modeling: Models and Reusability, 239–54. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11743-0_17.

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Moser, Raimund, Witold Pedrycz, Alberto Sillitti, and Giancarlo Succi. "A Model to Identify Refactoring Effort during Maintenance by Mining Source Code Repositories." In Product-Focused Software Process Improvement, 360–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69566-0_29.

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Sillitti, Alberto, and Giancarlo Succi. "Source Code Repositories and Agile Methods." In Extreme Programming and Agile Processes in Software Engineering, 243–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499053_37.

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Mukherjee, Sudipta. "Code Mining." In Source Code Analytics With Roslyn and JavaScript Data Visualization, 91–130. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1925-6_5.

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Diamantopoulos, Themistoklis, and Andreas L. Symeonidis. "Mining Source Code for Component Reuse." In Advanced Information and Knowledge Processing, 133–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30106-4_6.

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Diamantopoulos, Themistoklis, and Andreas L. Symeonidis. "Mining Source Code for Snippet Reuse." In Advanced Information and Knowledge Processing, 175–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-30106-4_7.

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Kato, Koki, Tsuyoshi Kanai, and Sanya Uehara. "Source Code Partitioning Using Process Mining." In Lecture Notes in Computer Science, 38–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23059-2_6.

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Savarimuthu, Bastin Tony Roy, and Hoa Khanh Dam. "Towards Mining Norms in Open Source Software Repositories." In Lecture Notes in Computer Science, 26–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55192-5_3.

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Pham, Hoang Son, Siegfried Nijssen, Kim Mens, Dario Di Nucci, Tim Molderez, Coen De Roover, Johan Fabry, and Vadim Zaytsev. "Mining Patterns in Source Code Using Tree Mining Algorithms." In Discovery Science, 471–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33778-0_35.

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Noll, John, Dominik Seichter, and Sarah Beecham. "A Qualitative Method for Mining Open Source Software Repositories." In IFIP Advances in Information and Communication Technology, 256–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33442-9_18.

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Conference papers on the topic "Mining Source Code Repositories"

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Dyer, Robert, Hoan Anh Nguyen, Hridesh Rajan, and Tien N. Nguyen. "Mining source code repositories with boa." In the 2013 companion publication for conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2508075.2514570.

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Gerlec, Crt, Andrej Krajnc, Marjan Hericko, and Jan Boznik. "Mining source code changes from software repositories." In 2011 7th Central and Eastern European Software Engineering Conference in Russia (CEE-SECR 2011). IEEE, 2011. http://dx.doi.org/10.1109/cee-secr.2011.6188468.

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Sokol, Francisco Zigmund, Mauricio Finavaro Aniche, and Marco Aurelio Gerosa. "MetricMiner: Supporting researchers in mining software repositories." In 2013 IEEE 13th International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2013. http://dx.doi.org/10.1109/scam.2013.6648195.

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Canfora, Gerardo, Luigi Cerulo, and Massimiliano Di Penta. "Identifying Changed Source Code Lines from Version Repositories." In Fourth International Workshop on Mining Software Repositories (MSR 2007). IEEE, 2007. http://dx.doi.org/10.1109/msr.2007.14.

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Kagdi, Huzefa, Michael L. Collard, and Jonathan I. Maletic. "Comparing Approaches to Mining Source Code for Call-Usage Patterns." In Fourth International Workshop on Mining Software Repositories. IEEE, 2007. http://dx.doi.org/10.1109/msr.2007.3.

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Allamanis, Miltiadis, and Charles Sutton. "Mining source code repositories at massive scale using language modeling." In 2013 10th IEEE Working Conference on Mining Software Repositories (MSR 2013). IEEE, 2013. http://dx.doi.org/10.1109/msr.2013.6624029.

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Efstathiou, Vasiliki, and Diomidis Spinellis. "Semantic Source Code Models Using Identifier Embeddings." In 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR). IEEE, 2019. http://dx.doi.org/10.1109/msr.2019.00015.

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Yusof, Y. "Template mining in source-code digital libraries." In "International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering. IEE, 2004. http://dx.doi.org/10.1049/ic:20040489.

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Sinha, Vibha Singhal, Diptikalyan Saha, Pankaj Dhoolia, Rohan Padhye, and Senthil Mani. "Detecting and Mitigating Secret-Key Leaks in Source Code Repositories." In 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories (MSR). IEEE, 2015. http://dx.doi.org/10.1109/msr.2015.48.

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Akbar, Shayan, and Avinash Kak. "SCOR: Source Code Retrieval with Semantics and Order." In 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR). IEEE, 2019. http://dx.doi.org/10.1109/msr.2019.00012.

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Reports on the topic "Mining Source Code Repositories"

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Xie, Tao. Mining Program Source Code for Improving Software Quality. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada581476.

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You might also be interested in the extended bibliographies on the topic 'Mining Source Code Repositories' for particular source types:
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