In the course of studying the effects of programming in multiple languages, we unearthed troubling trends in SourceForge artifacts. Our initial studies suggest that programming in multiple languages concurrently negatively affects developer productivity. While addressing our initial question of interest, we discovered a pattern of monolithic commits in the SourceForge community. Consequently, we also report on the effects that this pattern of commits can have when using SourceForge as a data-source for temporal analysis of open source projects or for studies of individual developers.

Conventional wisdom, inspired in part by Eric Raymond, suggests that open source developers primarily develop software for developers like themselves. In our studies we distinguish between reflexive software (software written primarily for other developers) and irreflexive software (software written primarily for passive users). In the first study, we present four criteria which we then use to assess project reflexivity in SourceForge. These criteria are based on three specific indicators: intended audience, relevant topics, and supported operating systems. Based on our criteria, we find that 68% of SourceForge projects are reflexive (in the sense described by Raymond). In the second study, we randomly sample and statically estimate reflexivity within SourceForge. Our results support Raymond's assertions that 1) OSS projects tend to be reflexive and 2) reflexive OSS projects tend to be more successful than irreflexive projects. We also find a decrease in reflexivity from a high in 2001 to a low in 2011.

In an effort to increase both the quality of software applications and the efficiency with which applications can be written, developers often incorporate multiple programming languages into software projects. Although language specialization arguably introduces benefits, the total impact of the resulting language fragmentation (working concurrently in multiple programming languages) on developer performance is unclear. For instance, developers may solve problems more efficiently when they have multiple language paradigms at their disposal. However, the overhead of maintaining efficiency in more than one language may outweigh those benefits. This thesis represents a first step toward understanding the relationship between language fragmentation and programmer productivity. We address that relationship within two different contexts: 1) the individual developer, and 2) the overall project. Using a data-centered approach, we 1) develop metrics for measuring productivity and language fragmentation, 2) select data suitable for calculating the needed metrics, 3) develop and validate statistical models that isolate the correlation between language fragmentation and individual programmer productivity, 4) develop additional methods to mitigate threats to validity within the developer context, and 5) explore limitations that need to be addressed in future work for effective analysis of language fragmentation within the project context using the SourceForge data set. Finally, we demonstrate that within the open source software development community, SourceForge, language fragmentation is negatively correlated with individual programmer productivity.

Software development is a process fraught with unpredictability, in part because software is created by people. Human interactions add complexity to development processes, and collaborative development can become a liability if not properly understood and managed. Recent years have seen an increase in the use of data mining techniques on publicly-available repository data with the goal of improving software development processes, and by extension, software quality. In this thesis, we introduce the concept of author entropy as a metric for quantifying interaction and collaboration (both within individual files and across projects), present results from two empirical observational studies of open-source projects, identify and analyze authorship and collaboration patterns within source code, demonstrate techniques for visualizing authorship patterns, and propose avenues for further research.

Artifact-based research provides a mechanism whereby researchers may study the creation of software yet avoid many of the difficulties of direct observation and experimentation. Open source software forges are of great value to the software researcher, because they expose many of the artifacts of software development. However, many challenges affect the quality of artifact-based studies, especially those studies examining software evolution. This thesis addresses one of these threats: the presence of very large commits, which we refer to as "Cliff Walls." Cliff walls are a threat to studies of software evolution because they do not appear to represent incremental development. In this thesis we demonstrate the existence of cliff walls in open source software projects and discuss the threats they present. We also seek to identify key causes of these monolithic commits, and begin to explore ways that researchers can mitigate the threats of cliff walls.

The thesis deals with OpenCV library and its implementation into scripting language Lua. The first part of the thesis concentrates on description of the course Computer vision MPOV and description of mathematical basics needed for further understandings. The second part describes OpenCV library and its potential usage in the MPOV. Next chapter examines the programming scripting language Lua. The description of the implementation of binding the OpenCV library to Lua language along with its overall functionality is included in the practical part of the thesis. The use of LuaCV is more comfortable thanks to Open Source projects for cross-platform compilation and distribution. Part of the thesis is also generator of Latex documentation for LuaCV binding. The last chapter deals with testing LuaCV in course MPOV and analysis of criticism from students.