@conference {bird2010lee, title = {{Linkster: Enabling Efficient Manual Mining}}, booktitle = {Demonstration Track, Proceedings of the 17th SIGSOFT Symposium on Foundations of Software Engineering}, year = {2010}, note = {"LINKSTER efficiently displays, integrates, and allows inspection and annotation of information from three main sources of data: source code repositories, developer mailing lists archives, and bug tracking databases. LINKSTER requires access to a source code repository for file content and a database which contains the raw mined repository, mailing list, and bug tracking information. All notes and annotations made by the user are also recorded in the database."}, publisher = {ACM}, organization = {ACM}, abstract = {While many uses of mined software engineering data are automatic in nature, some techniques and studies either require, or can be improved, by manual methods. Unfortunately, manually inspecting, analyzing, and annotating mined data can be difficult and tedious, especially when information from multiple sources must be integrated. Oddly, while there are numerous tools and frameworks for automatically mining and analyzing data, there is a dearth of tools which facilitate manual methods. To fill this void, we have developed LINKSTER, a tool which integrates data from bug databases, source code repositories, and mailing list archives to allow manual inspection and annotation. LINKSTER has already been used successfully by an OSS project lead to obtain data for one empirical study.}, keywords = {artifacts, bug, bug tracking, data mining, email, mailing lists, open source, source code}, attachments = {https://flosshub.org/sites/flosshub.org/files/bird2010lee.pdf}, author = {Christian Bird and Adrian Bachman and Rahman, Foyzur and Bernstein, Abraham} } @conference {Knab:2006:PDD:1137983.1138012, title = {Predicting defect densities in source code files with decision tree learners}, booktitle = {Proceedings of the 2006 international workshop on Mining software repositories}, series = {MSR {\textquoteright}06}, year = {2006}, pages = {119{\textendash}125}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {With the advent of open source software repositories the data available for defect prediction in source files increased tremendously. Although traditional statistics turned out to derive reasonable results the sheer amount of data and the problem context of defect prediction demand sophisticated analysis such as provided by current data mining and machine learning techniques.In this work we focus on defect density prediction and present an approach that applies a decision tree learner on evolution data extracted from the Mozilla open source web browser project. The evolution data includes different source code, modification, and defect measures computed from seven recent Mozilla releases. Among the modification measures we also take into account the change coupling, a measure for the number of change-dependencies between source files. The main reason for choosing decision tree learners, instead of for example neural nets, was the goal of finding underlying rules which can be easily interpreted by humans. To find these rules, we set up a number of experiments to test common hypotheses regarding defects in software entities. Our experiments showed, that a simple tree learner can produce good results with various sets of input data.}, keywords = {change analysis, data mining, decision tree learner, defect density, defect prediction, mozilla, prediction, release history, scm, source code, version control}, isbn = {1-59593-397-2}, doi = {http://doi.acm.org/10.1145/1137983.1138012}, url = {http://doi.acm.org/10.1145/1137983.1138012}, attachments = {https://flosshub.org/sites/flosshub.org/files/119Predicting.pdf}, author = {Knab, Patrick and Pinzger, Martin and Bernstein, Abraham} }