@conference {zanjani2015developer, title = {Using Developer-Interaction Trails to Triage Change Requests}, booktitle = {12th Working Conference on Mining Software Repositories (MSR 2015)}, year = {2015}, month = {05/2015}, publisher = {IEEE}, organization = {IEEE}, abstract = {The paper presents an approach, namely iHDev, to recommend developers who are most likely to implement incoming change requests. The basic premise of iHDev is that the developers who interacted with the source code relevant to a given change request are most likely to best assist with its resolution. A machine-learning technique is first used to locate source-code entities relevant to the textual description of a given change request. iHDev then mines interaction trails (i.e., Mylyn sessions) associated with these source-code entities to recommend a ranked list of developers. iHDev integrates the interaction trails in a unique way to perform its task, which was not investigated previously. An empirical study on open source systems Mylyn and Eclipse Project was conducted to assess the effectiveness of iHDev. A number of change requests were used in the evaluated benchmark. Recall for top one to five recommended developers and Mean Reciprocal Rank (MRR) values are reported. Furthermore, a comparative study with two previous approaches that use commit histories and/or the source-code authorship information for developer recommendation was performed. Results show that iHDev could provide a recall gain of up to 127.27\% with equivalent or improved MRR values by up to 112.5\%.}, url = {http://www.cabird.com/wp/zanjani2015developer/}, attachments = {https://flosshub.org/sites/flosshub.org/files/zanjani2015developer.pdf}, author = {Motahareh Bahrami Zanjani and Kagdi, Huzefa and Christian Bird} } @proceedings {1502, title = {A Dataset from Change History to Support Evaluation of Software Maintenance Tasks}, year = {2013}, month = {05/2013}, pages = {131-134}, abstract = {Approaches that support software maintenance need to be evaluated and compared against existing ones, in order to demonstrate their usefulness in practice. However, oftentimes the lack of well-established sets of benchmarks leads to situations where these approaches are evaluated using different datasets, which results in biased comparisons. In this data paper we describe and make publicly available a set of benchmarks from six Java applications, which can be used in the evaluation of various software engineering (SE) tasks, such as feature location and impact analysis. These datasets consist of textual description of change requests, the locations in the source code where they were implemented, and execution traces. Four of the benchmarks were already used in several SE research papers, and two of them are new. In addition, we describe in detail the methodology used for generating these benchmarks and provide a suite of tools in order to encourage other researchers to validate our datasets and generate new benchmarks for other subject software systems. Our online appendix: http://www.cs.wm.edu/semeru/data/msr13/ }, url = {http://www.cs.wm.edu/~bdit/publications/MSR13DataPaper_Dit_CRC.pdf}, attachments = {https://flosshub.org/sites/flosshub.org/files/MSR13DataPaper_Dit_CRC.pdf}, author = {Bogdan Dit and Andrew Holtzhauer and Poshyvanyk, Denys and Kagdi, Huzefa} } @conference {1005, title = {Comparing Approaches to Mining Source Code for Call-Usage Patterns}, booktitle = {Fourth International Workshop on Mining Software Repositories (MSR{\textquoteright}07:ICSE Workshops 2007)}, year = {2007}, pages = {20 - 20}, publisher = {IEEE}, organization = {IEEE}, address = {Minneapolis, MN, USA}, abstract = {Two approaches for mining function-call usage patterns from source code are compared. The first approach, itemset mining, has recently been applied to this problem. The other approach, sequential-pattern mining, has not been previously applied to this problem. Here, a call-usage pattern is a composition of function calls that occur in a function definition. Both approaches look for frequently occurring patterns that represent standard usage of functions and identify possible errors. Itemset mining produces unordered patterns, i.e., sets of function calls, whereas, sequential-pattern mining produces partially ordered patterns, i.e., sequences of function calls. The trade-off between the additional ordering context given by sequential-pattern mining and the efficiency of itemset mining is investigated. The two approaches are applied to the Linux kernel v2.6.14 and results show that mining ordered patterns is worth the additional cost.}, keywords = {function calls, functions, kernel, linux, sequence, sequencing, sequential-pattern mining}, isbn = {0-7695-2950-X}, doi = {10.1109/MSR.2007.3}, attachments = {https://flosshub.org/sites/flosshub.org/files/28300020.pdf}, author = {Kagdi, Huzefa and Collard, Michael L. and Maletic, Jonathan I.} } @conference {Kagdi:2006:MSC:1137983.1137996, title = {Mining sequences of changed-files from version histories}, booktitle = {Proceedings of the 2006 international workshop on Mining software repositories}, series = {MSR {\textquoteright}06}, year = {2006}, pages = {47{\textendash}53}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {Modern source-control systems, such as Subversion, preserve change-sets of files as atomic commits. However, the specific ordering information in which files were changed is typically not found in these source-code repositories. In this paper, a set of heuristics for grouping change-sets (i.e., log-entries) found in source-code repositories is presented. Given such groups of change-sets, sequences of files that frequently change together are uncovered. This approach not only gives the (unordered) sets of files but supplements them with (partial temporal) ordering information. The technique is demonstrated on a subset of KDE source-code repository. The results show that the approach is able to find sequences of changed-files.}, keywords = {change, change history, change management, change sequences, heuristics, kde, mining software repositories, scm, sequences, source code}, isbn = {1-59593-397-2}, doi = {http://doi.acm.org/10.1145/1137983.1137996}, url = {http://doi.acm.org/10.1145/1137983.1137996}, attachments = {https://flosshub.org/sites/flosshub.org/files/47MiningSequences.pdf}, author = {Kagdi, Huzefa and Yusuf, Shehnaaz and Maletic, Jonathan I.} }