Optimization Scenarios for Open Source Software Used in E-Learning Activities

Using open software in e-learning application is one of the most popular ways of improving effectiveness of e-learning-based processes without thinking about additional costs and even focusing on modifying the software according to needs. Because of that, it is important to have an idea about what is needed while using an e-learning-oriented open software system and how to deal with its source codes. At this point, it is a good option to add some additional features and functions to make the open source software more intelligent and practical to make both teaching-learning experiences during e-learning processes. In this context, the objective of this chapter is to discuss some possible applications of artificial intelligence to include optimization processes within open source software systems used in e-learning activities. In detail, the chapter focuses more on using swarm intelligence and machine learning techniques for this aim and expresses some theoretical views for improving the effectiveness of such software for a better e-learning experience.

Logging Analysis and Prediction in Open Source Java Project

Log statements present in source code provide important information to the software developers because they are useful in various software development activities such as debugging, anomaly detection, and remote issue resolution. Most of the previous studies on logging analysis and prediction provide insights and results after analyzing only a few code constructs. In this chapter, the authors perform an in-depth, focused, and large-scale analysis of logging code constructs at two levels: the file level and catch-blocks level. They answer several research questions related to statistical and content analysis. Statistical and content analysis reveals the presence of differentiating properties among logged and nonlogged code constructs. Based on these findings, the authors propose a machine-learning-based model for catch-blocks logging prediction. The machine-learning-based model is found to be effective in catch-blocks logging prediction.

Demography of Open Source Software Prediction Models and Techniques

Open source software (OSS) is currently a widely adopted approach to developing and distributing software. Many commercial companies are using OSS components as part of their product development. For instance, more than 58% of web servers are using an OSS web server, Apache. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This chapter reports on a systematic literature survey aimed at the identification and structuring of research that offers prediction models and techniques in analysing OSS projects. The study outcome provides insight into what constitutes the main contributions of the field, identifies gaps and opportunities, and distils several important future research directions. This chapter extends the authors' earlier journal article and offers the following improvements: broader study period, enhanced discussion, and synthesis of reported results.

Towards Sustainable Development Through Open Source Software in the Arab World

A literature survey study was conducted to explore the state-of-the-art of open source software and the opportunities and challenges faced by this segment of the software industry in seven Arab countries: Tunisia, Egypt, Jordan, Saudi Arabia, Qatar, Oman, and UAE. A framework and road map for OSS is derived and presented from interviews conducted in the UAE with at least four experts from each of the following categories: governments and ministries, IT companies, universities, and IT enthusiasts. This is the first study of its kind in this part of the world and is expected to make a significant contribution to the direction for open source software in the region and beyond.

Open Growth

Open source software (OSS) is well established in sectors as diverse as aviation, health, telecommunications, finance, publishing, education, and government. As nations increasingly rely on knowledge assets to grow, the adoption of OSS will have profound economic consequences. This chapter identifies the mechanisms inherent to OSS production that help fuel innovation in knowledge-based economies. As a collaborative and open production model, OSS is conceptualized as a prototype of open innovation. OSS-related software development jobs are widely diffused throughout the economy, help build a skilled labour force, and offer wages significantly above the national average. OSS is thus believed to be a strong contributor to growth in high-value employment in the US. The authors also posit that, as industries are exposed to the benefits of OSS as a result of the broad diffusion of OSS-related jobs, open innovation processes outside software development may be adopted through a process of learning and imitation.

Unlocking the Unlimited Potentials of Koha OSS/ILS for Library House-Keeping Functions

Drawing praxis from Bowen University, Nigeria and other libraries worldwide, the chapter unveils the limitless capabilities of Koha ILS to successfully manage core library house-keeping functions—cataloging, acquisitions, circulation control, patrons' management, OPAC, serials, and report generation—in one seamless whole. Web-based features like its flexibility, adaptability, interoperability, MARC, Z39.50, patrons' ability for online logging in, registration, renewal, and many more were revealed. Also, reasons for its global adoptability, benefits, likely challenges, and solutions from practitioners were also highlighted. The chapter concludes that despite the puny but eventually surmounted challenges, Koha holds unlimited potential for libraries of any shape/size by just garnering from the experiences of subsisting users of the software globally.

A Systematic Review of Attributes and Techniques for Open Source Software Evolution Analysis

Many studies have been conducted to understand the evolution process of Open Source Software (OSS). The researchers have used various techniques for understanding the OSS evolution process from different perspectives. This chapter reports a meta-data analysis of the systematic literature review on the topic in order to understand its current state and to identify opportunities for the future. This research identified 190 studies, selected against a set of questions, for discussion. It categorizes the research studies into nine categories. Based on the results obtained from the systematic review, there is evidence of a shift in the metrics and methods for OSS evolution analysis over the period of time. The results suggest that there is a lack of a uniform approach to analyzing and interpreting the results. There is need of more empirical work using a standard set of techniques and attributes to verify the phenomenon governing the OSS projects. This will help to advance the field and establish a theory of software evolution.

Strategy of Good Software Governance

To chapter concerns emerging cybernetics, which is the school of “meaning to lead” and is particularly associated with the idea of dominations and controls. This chapter initially anatomizes the sociology of software cybernetics into two broad movements—free/libre and open source software (FLOSS) and proprietary close source software (PCSS)—to argue a good software governance approach. This chapter discusses (a) in what matters and (b) for what reasons software governance of Turkey has locked into the ecosystems of PCSS and, in particular, considers causes, effects, and potential outcomes of not utilizing FLOSS in the state of Turkey. The government has continuously stated that there are no compulsory national or international conventions(s) and settlement(s) with the ecosystems of PCSS and that there is no vendor lock-in concern. Nevertheless, the chapter principally argues that Turkey has taken a pragmatic decision-making process of software in the emerging cybernetics that leads and contributes to techno-social externality of PCSS hegemonic stability.

Open Source Projects Recommendation on GitHub

This chapter discusses the design and the implementation of a recommender system for open source projects on GitHub using the collaborative-filtering approach. Having such a system can be helpful for many developers, especially those who search for a particular project based on their interests. It can also reduce searching time and make search results more relevant. The system presented in this chapter was evaluated on a real-world dataset and using various evaluation metrics. Results obtained from these experiments are very promising. The authors found that their recommender system can reach better precision and recall accuracy.