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|Title: ||MINING UNSTRUCTURED SOFTWARE REPOSITORIES USING IR MODELS|
|Authors: ||Thomas, STEPHEN|
|Keywords: ||empirical studies|
mining software repositories
|Issue Date: ||12-Dec-2012|
|Series/Report no.: ||Canadian theses|
|Abstract: ||Mining Software Repositories, which is the process of analyzing the data related
to software development practices, is an emerging field which aims to
aid development teams in their day to day tasks. However, data in many
software repositories is currently unused because the data is unstructured, and therefore
difficult to mine and analyze. Information Retrieval (IR) techniques, which were developed
specifically to handle unstructured data, have recently been used by researchers to mine
and analyze the unstructured data in software repositories, with some success.
The main contribution of this thesis is the idea that the research and practice of using
IR models to mine unstructured software repositories can be improved by going beyond the
current state of affairs. First, we propose new applications of IR models to existing software
engineering tasks. Specifically, we present a technique to prioritize test cases based on their
IR similarity, giving highest priority to those test cases that are most dissimilar. In another
new application of IR models, we empirically recover how developers use their mailing list
while developing software.
Next, we show how the use of advanced IR techniques can improve results. Using a
framework for combining disparate IR models, we find that bug localization performance
can be improved by 14–56% on average, compared to the best individual IR model. In
addition, by using topic evolution models on the history of source code, we can uncover the
evolution of source code concepts with an accuracy of 87–89%.
Finally, we show the risks of current research, which uses IR models as black boxes without
fully understanding their assumptions and parameters. We show that data duplication
in source code has undesirable effects for IR models, and that by eliminating the duplication,
the accuracy of IR models improves. Additionally, we find that in the bug localization
task, an unwise choice of parameter values results in an accuracy of only 1%, where optimal
parameters can achieve an accuracy of 55%.
Through empirical case studies on real-world systems, we show that all of our proposed
techniques and methodologies significantly improve the state-of-the-art.|
|Description: ||Thesis (Ph.D, Computing) -- Queen's University, 2012-12-12 12:34:59.854|
|Appears in Collections:||Computing Graduate Theses|
Queen's Theses & Dissertations
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