@article {thomas2014studying,
	title = {Studying software evolution using topic models},
	journal = {Science of Computer Programming},
	volume = {80},
	year = {2014},
	pages = {457{\textendash}479},
	publisher = {Elsevier},
	abstract = {Topic models are generative probabilistic models which have been applied to information
retrieval to automatically organize and provide structure to a text corpus. Topic models
discover topics in the corpus, which represent real world concepts by frequently cooccurring
words. Recently, researchers found topics to be effective tools for structuring
various software artifacts, such as source code, requirements documents, and bug reports.
This research also hypothesized that using topics to describe the evolution of software
repositories could be useful for maintenance and understanding tasks. However, research
has yet to determine whether these automatically discovered topic evolutions describe the
evolution of source code in a way that is relevant or meaningful to project stakeholders, and
thus it is not clear whether topic models are a suitable tool for this task.
In this paper, we take a first step towards evaluating topic models in the analysis of
software evolution by performing a detailed manual analysis on the source code histories
of two well-known and well-documented systems, JHotDraw and jEdit. We define and
compute various metrics on the discovered topic evolutions and manually investigate how
and why the metrics evolve over time. We find that the large majority (87\%{\textendash}89\%) of topic
evolutions correspond well with actual code change activities by developers. We are thus
encouraged to use topic models as tools for studying the evolution of a software system.},
	keywords = {Latent Dirichlet allocation, mining software repositories, software evolution, topic model},
	url = {http://sail.cs.queensu.ca/publications/pubs/Thomas-2012-SCP.pdf},
	author = {Stephen W. Thomas and Adams, Bram and Hassan, Ahmed E. and Blostein, Dorothea}
}
@conference {zmzkh-icsm-2013,
	title = {How does Context affect the Distribution of Software Maintainability Metrics?},
	booktitle = {Proceedings of the 29th IEEE International Conference on Software Maintainability},
	series = {ICSM {\textquoteright}13},
	year = {2013},
	note = {"FLOSSMole [25] is another data source, from
where we download descriptions (i.e., application domain)
of SourceForge software systems. Furthermore, we download
latest application domain information4
and monthly download
data5 of studied software systems directly from SourceForge."},
	abstract = {Software metrics have many uses, e.g., defect prediction, effort estimation, and benchmarking an organization against peers and industry standards. In all these cases, metrics may depend on the context, such as the programming language. Here we aim to investigate if the distributions of commonly used metrics do, in fact, vary with six context factors: application domain, programming language, age, lifespan, the number of changes, and the number of downloads. For this preliminary study we select 320 nontrivial software systems from SourceForge. These software systems are randomly sampled from nine popular application domains of SourceForge. We calculate 39 metrics commonly used to assess software maintainability for each software system and use Kruskal Wallis test and Mann-Whitney U test to determine if there are significant differences among the distributions with respect to each of the six context factors. We use Cliff{\textquoteright}s delta to measure the magnitude of the differences and find that all six context factors affect the distribution of 20 metrics and the programming language factor affects 35 metrics. We also briefly discuss how each context factor may affect the distribution of metric values.We expect our results to help software benchmarking and other software engineering methods that rely on these commonly used metrics to be tailored to a particular context.},
	keywords = {benchmark, context, contextual factor, flossmole, large scale, metrics, mining software repositories, sampling, software maintainability, sourceforge, static metrics},
	attachments = {https://flosshub.org/sites/flosshub.org/files/icsm2013_contextstudy.pdf},
	author = {Zhang, Feng and Audris Mockus and Ying Zou and Foutse Khomh and Hassan, Ahmed E.}
}
