Decision Point Analysis on Learning Process Models in FLOSS mailing Archives

Abstract. Numerous studies continue to explore the potential of social interactions between people in Free/Libre Open Source Software (FLOSS) environments. While the dynamics of interactions in these environments can be understood from different perspectives, we put a particular focus on any interactions resulting in knowledge transfer and acquisition. As learning platforms, FLOSS communities provide immense opportunities for improving software engineering skills. People who engage in FLOSS activities both acquire and improve their software development skills. For this reason, it is very helpful to understand how these learning interactions occur. In this paper, we make use of the decision miner in process mining to conduct our analysis. The purpose of such an endeavour is twofold. Firstly, we provide empirical insights into how people learn while exchanging emails in FLOSS mailing archives. Lastly, we go a step further by providing insights behind the motivation into learning participants' decisions on their learning paths

Mining Educational Social Network Structures from FLOSS Repositories

FLOSS environments have been proved to provide an interesting learning platform for software engineers. Research suggests that people partaking in both technical and non-technical activities in FLOSS prjects are more likely to positively improve their software engineering skills. To this end, there are propositions to involve computer science and software engineering students in formal higher institutions of learning, in participating in FLOSS projects in order to give them an opportunity to develop their programming capacity by working on real-life projects. While some empirical studies have been conducted to provide some lights on learning processes in FLOSS environments, there is limited or no work done pertaining to understanding social structures during this process of knowledge transfer and acquisition. In this paper, we make use of social network analysis techniques in order to provide insights related to the emerging of social structures from FLOSS repositories from an educational point of view. We hope that these educational structures will enhance both the understanding with regards to how learning occurs in these communities and especially, the frequency of participants' involvement that culminates into learning

Mining Educational Social Network Structures from FLOSS Repositories

FLOSS environments have been proved to provide an interesting learning platform for software engineers. Research suggests that people partaking in both technical and non-technical activities in FLOSS prjects are more likely to positively improve their software engineering skills. To this end, there are propositions to involve computer science and software engineering students in formal higher institutions of learning, in participating in FLOSS projects in order to give them an opportunity to develop their programming capacity by working on real-life projects. While some empirical studies have been conducted to provide some lights on learning processes in FLOSS environments, there is limited or no work done pertaining to understanding social structures during this process of knowledge transfer and acquisition. In this paper, we make use of social network analysis techniques in order to provide insights related to the emerging of social structures from FLOSS repositories from an educational point of view. We hope that these educational structures will enhance both the understanding with regards to how learning occurs in these communities and especially, the frequency of participants' involvement that culminates into learning

Process Models for Learning Patterns in FLOSS Repositories

Evidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of these environments’ repositories. To date, numerous studies document the existence of learning processes in FLOSS through surveys or by means of questionnaires filled by FLOSS projects participants. At the same time, there is a surge in developing tools and techniques for extracting and analyzing data from different FLOSS data sources that has birthed a new field called Mining Software Repositories (MSR). In spite of these growing tools and techniques for mining FLOSS repositories, there is limited or no existing approaches to providing empirical evidence of learning processes directly from these repositories. Therefore, in this work we sought to trigger such an initiative by proposing an approach based on Process Mining. With this technique, we aim to trace learning behaviors from FLOSS participants’ trails of activities as recorded in FLOSS repositories. We identify the participants as Novices and Experts. A Novice is defined as any FLOSS member that benefits from a learning experience through acquiring new skills while the Expert is the provider of these skills. The significance of our work is mainly twofold. First and foremost, we extend the MSR field by showing the potential of mining FLOSS repositories by applying Process Mining techniques. Lastly, our work provides critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the relevant repositories. In order to accomplish this, we have proposed and implemented a methodology that follows a seven-step approach including developing an appropriate terminology or ontology for learning processes in FLOSS, contextualizing learning processes through a-priori models, generating Event Logs, generating corresponding process models, interpreting and evaluating the value of process discovery, performing conformance analysis and verifying a number of formulated hypotheses with regard to tracing learning patterns in FLOSS communities. The implementation of this approach has resulted in the development of the Ontology of Learning in FLOSS (OntoLiFLOSS) environments that defines the terms needed to describe learning processes in FLOSS as well as providing a visual representation of these processes through Petri net-like Workflow nets. Moreover, another novelty pertains to the mining of FLOSS repositories by defining and describing the preliminaries required for preprocessing FLOSS data before applying Process Mining techniques for analysis. Through a step-by-step process, we effectively detail how the Event Logs are constructed through generating key phrases and making use of Semantic Search. Taking a FLOSS environment called Openstack as our data source, we apply our proposed techniques to identify learning activities based on key phrases catalogs and classification rules expressed through pseudo code as well as the appropriate Process Mining tool. We thus produced Event Logs that are based on the semantic content of messages in Openstack’s Mailing archives, Internet Relay Chat (IRC) messages, Reviews, Bug reports and Source code to retrieve the corresponding activities. Considering these repositories in light of the three learning process phases (Initiation, Progression and maturation), we produced an Event Log for each participant (Novice or Expert) in every phase on the corresponding dataset. Hence, we produced 14 Event Logs that helped build 14 corresponding process maps which are visual representation of the flow occurrence of learning activities in FLOSS for each participant. These process maps provide critical indications that speak volumes in terms of the presence of learning processes in the analyzed repositories. The results show that learning activities do occur at a significant rate during messages exchange on both Mailing archives and IRC messages. The slight differences between the two datasets can be highlighted in two ways. First, the involvement of Experts is more on iv IRC than it is on Mailing archives with 7.22% and 0.36% of Expert involvement respectively on IRC forums and Mailing lists. This can be justified by the differences in the length of messages sent on these two datasets. The average length of sent messages is 3261 characters for an email compared to 60 characters for a chat message. The evidence produced from this mining experiment solidifies the finding in terms of the existence of learning processes in FLOSS as well as the scale at which they occur. While the Initiation phase shows the Novice as the most involved in the start of the learning process, during Progression phase the involvement of the Expert can be seen to be significantly increasing. In order to trace the advanced skills in the Maturation phase, we look at repositories that store data about developing, creating code, examining and reviewing the code, identifying and fixing possible bugs. Therefore, we consider three repositories including Source Code, Bug reports and Reviews. The results obtained in this phase largely justify the choice of these three datasets to track learning behavior at this stage. Both the Bug reports and the Source code demonstrate the commitment of the Novice to seek answers and interact as much as possible in strengthening the acquired skills. With a participation of 49.22% for the Novice against 46.72% for the Expert and 46.19 % against 42.04% respectively on Bug reports and Source code, the Novice still engages significantly in learning. On the last dataset, Reviews, we notice an increase in the Expert’s role. The Expert performs activities to the tune of 40.36 % of total number of activities against 22.17 % for the Novice. The last steps of our methodology steer the comparison of the defined a-priori models with final models that describe how learning processes occur according to the actual behavior from Event Logs. Our attempts to producing process models start with depicting process maps to track the actual behaviour as it occurs in Openstack repositories, before concluding with final Petri net models representative of learning processes in FLOSS as a result of conformance analysis. For every dataset in the corresponding learning phase, we produce 3 process maps respectively depicting the overall learning behaviour for all FLOSS community members (Novice or Expert together), then the Novice and Expert. In total, we produced 21 process maps, empirically describing process models on real data, 14 process models in the form of Petri nets for every participant on each dataset. We make use of the Artificial Immune System (AIS) algorithms to merge the 14 Event Logs that uniquely capture the behaviour of every participant on different datasets in the three phases. We then reanalyze the resulting logs in order to produce 6 global models that inclusively provide a comprehensive depiction of participants’ learning behavior in FLOSS communities. This description hints that Workflow nets introduced as our a-priori models give rather a more simplistic representation of learning processes in FLOSS. Nevertheless, our experiments with Event Logs starting from process discovery to conformance checking from Openstack repositories demonstrate that the real learning behaviors are more complete and most importantly largely submerge these simplistic a-priori models. Finally, our methodology has proved to be effective in both providing a novel alternative for mining FLOSS repositories and providing empirical evidence that describes how knowledge is exchanged in FLOSS environments. Moreover, our results enrich the MSR field by providing a reproducible step-by-step problem solving approach that can be customized to answer subsequent research questions in FLOSS repositories using Process Mining

Hierarchical categorisation of tags for delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.Preprin

Hierarchical categorisation of web tags for Delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. The benefits of social tagging are clear – tags enhance Web content browsing and search. However, since these tags may be publicly available to any Internet user, a privacy attacker may collect this information and extract an accurate snapshot of users’ interests or user profiles, containing sensitive information, such as health-related information, political preferences, salary or religion. In order to hinder attackers in their efforts to profile users, this report focuses on the practical aspects of capturing user interests from their tagging activity. More accurately, we study how to categorise a collection of tags posted by users in one of the most popular bookmarking services, Delicious (http://delicious.com).Preprin

Dependency Management 2.0 – A Semantic Web Enabled Approach

Software development and evolution are highly distributed processes that involve a multitude of supporting tools and resources. Application programming interfaces are commonly used by software developers to reduce development cost and complexity by reusing code developed by third-parties or published by the open source community. However, these application programming interfaces have also introduced new challenges to the Software Engineering community (e.g., software vulnerabilities, API incompatibilities, and software license violations) that not only extend beyond the traditional boundaries of individual projects but also involve different software artifacts. As a result, there is the need for a technology-independent representation of software dependency semantics and the ability to seamlessly integrate this representation with knowledge from other software artifacts. The Semantic Web and its supporting technology stack have been widely promoted to model, integrate, and support interoperability among heterogeneous data sources. This dissertation takes advantage of the Semantic Web and its enabling technology stack for knowledge modeling and integration. The thesis introduces five major contributions: (1) We present a formal Software Build System Ontology – SBSON, which captures concepts and properties for software build and dependency management systems. This formal knowledge representation allows us to take advantage of Semantic Web inference services forming the basis for a more flexibility API dependency analysis compared to traditional proprietary analysis approaches. (2) We conducted a user survey which involved 53 open source developers to allow us to gain insights on how actual developers manage API breaking changes. (3) We introduced a novel approach which integrates our SBSON model with knowledge about source code usage and changes within the Maven ecosystem to support API consumers and producers in managing (assessing and minimizing) the impacts of breaking changes. (4) A Security Vulnerability Analysis Framework (SV-AF) is introduced, which integrates builds system, source code, versioning system, and vulnerability ontologies to trace and assess the impact of security vulnerabilities across project boundaries. (5) Finally, we introduce an Ontological Trustworthiness Assessment Model (OntTAM). OntTAM is an integration of our build, source code, vulnerability and license ontologies which supports a holistic analysis and assessment of quality attributes related to the trustworthiness of libraries and APIs in open source systems. Several case studies are presented to illustrate the applicability and flexibility of our modelling approach, demonstrating that our knowledge modeling approach can seamlessly integrate and reuse knowledge extracted from existing build and dependency management systems with other existing heterogeneous data sources found in the software engineering domain. As part of our case studies, we also demonstrate how this unified knowledge model can enable new types of project dependency analysis

Media and Education in the Digital Age

This book is an invitation to informed and critical participation in the current debate on the role of digital technology in education and a comprehensive introduction to the most relevant issues in this debate. After an early wave of enthusiasm about the emancipative opportunities of the digital «revolution» in education, recent contributions invite caution, if not scepticism. This collection rejects extreme interpretations and establishes a conceptual framework for the critical questioning of this role in terms of concepts, assessments and subversions. This book offers conceptual tools, ideas and insights for further research. It also provides motivation and information to foster active participation in debates and politics and encourages teachers, parents and learners to take part in the making of the future of our societies