LASSO – an observatorium for the dynamic selection, analysis and comparison of software

Mining software repositories at the scale of 'big code' (i.e., big data) is a challenging activity. As well as finding a suitable software corpus and making it programmatically accessible through an index or database, researchers and practitioners have to establish an efficient analysis infrastructure and precisely define the metrics and data extraction approaches to be applied. Moreover, for analysis results to be generalisable, these tasks have to be applied at a large enough scale to have statistical significance, and if they are to be repeatable, the artefacts need to be carefully maintained and curated over time. Today, however, a lot of this work is still performed by human beings on a case-by-case basis, with the level of effort involved often having a significant negative impact on the generalisability and repeatability of studies, and thus on their overall scientific value. The general purpose, 'code mining' repositories and infrastructures that have emerged in recent years represent a significant step forward because they automate many software mining tasks at an ultra-large scale and allow researchers and practitioners to focus on defining the questions they would like to explore at an abstract level. However, they are currently limited to static analysis and data extraction techniques, and thus cannot support (i.e., help automate) any studies which involve the execution of software systems. This includes experimental validations of techniques and tools that hypothesise about the behaviour (i.e., semantics) of software, or data analysis and extraction techniques that aim to measure dynamic properties of software. In this thesis a platform called LASSO (Large-Scale Software Observatorium) is introduced that overcomes this limitation by automating the collection of dynamic (i.e., execution-based) information about software alongside static information. It features a single, ultra-large scale corpus of executable software systems created by amalgamating existing Open Source software repositories and a dedicated DSL for defining abstract selection and analysis pipelines. Its key innovations are integrated capabilities for searching for selecting software systems based on their exhibited behaviour and an 'arena' that allows their responses to software tests to be compared in a purely data-driven way. We call the platform a 'software observatorium' since it is a place where the behaviour of large numbers of software systems can be observed, analysed and compared

Trust networks for recommender systems

Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.) We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators. In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections

Comparison between Continuous Integration tools

Nowadays many tasks are performed by the help of various software in each aspect of our life. This software involvement becomes deeper and deeper every day, which leads to a need to release products faster to the market, considering the intense competition among companies. For this reason, companies start to enhance benefits of development models with use of Continuous Integration. Today, there are many CI tools available on the market that can be used for the software integration process and choosing right tool is becoming a hard task. During this research, it was clarified and shown the overview of most common CI instruments. Then, it was performed comparison and was provided decision matrix for Continuous Integration frameworks. Based on comparison, Jenkins was preferred over other tools, since it is a free open-source project with providing great flexibility to many different development methodologies. This Continuous Integration framework fits to majority defined requirements and meets our thesis research questions. In addition, this thesis project can be helpful as short guide, where is suited basic knowledge for starting work with CI and it allows to dive into CI process, and learn in parallel from where it takes origins

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Consumer-to-consumer influence on attitude towards, and intention to buy South Korean food: an Instagram study.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Background – With the emergence of the internet and the introduction of social networking services, opportunities for consumer-to-consumer (C2C) communication have expanded exponentially. Virtual communities enable consumers to access and exchange information with each other, independent of companies, allowing consumers to influence each other’s attitudes and behaviours. Instagram, a free photo and video sharing mobile application, is a platform with great potential and growth and has caught the attention of marketers seeking to understand and use C2C influence. With the increase of user-generated content (UGC), interest has shifted onto consumers and what factors influence them in a C2C context. Purpose – The purpose of this research was to investigate the factors of Source Credibility, Tie Strength, Informational Quality and Consumer Susceptibility to Interpersonal Influence (CSII) in the C2C context of Instagram and their effect on consumers attitudes towards and intentions to purchase South Korean food. The contribution is for marketers to better understand C2C influence. Research Methodology/Approach – A quantitative approach involved an Instagram account called @shaynanigans_sk being specifically set up and images and videos of South Korean food consistently posted. Research data on the constructs in the conceptual framework was collected using an online survey, with a total of 163 Instagram followers who made up the final sample. Findings – The research found that Source Credibility and CSII significantly impact consumers’ attitudes towards South Korean food, and Informational Quality was found to significantly impact consumers’ purchase intentions towards South Korean food. Managerial implications – Marketing practitioners spend a significant amount of time determining the driving forces that influence consumer behaviour. The outcomes of this study confirmed that the relationships between Source Credibility, CSII, and Attitude; and Informational Quality and Intention were significant. These results emphasise the need for marketers using consumers in Influencer marketing strategies on Instagram to consider and pay attention to the chosen consumers’ credibility, their content quality, and the types of other consumers they target, as these factors will influence the success of Influencer marketing on this platform. Research limitations – Limitations and recommendations for future research are identified

Flexible cross layer optimization for fixed and mobile broadband telecommunication networks and beyond

In der heutigen Zeit, in der das Internet im Allgemeinen und Telekommunikationsnetze im Speziellen kritische Infrastrukturen erreicht haben, entstehen hohe Anforderungen und neue Herausforderungen an den Datentransport in Hinsicht auf Effizienz und Flexibilität. Heutige Telekommunikationsnetze sind jedoch rigide und statisch konzipiert, was nur ein geringes Maß an Flexibilität und Anpassungsfähigkeit der Netze ermöglicht und darüber hinaus nur im begrenzten Maße die Wichtigkeit von Datenflüssen im wiederspiegelt. Diverse Lösungsansätze zum kompletten Neuentwurf als auch zum evolutionären Konzept des Internet wurden ausgearbeitet und spezifiziert, um diese neuartigen Anforderungen und Herausforderungen adäquat zu adressieren. Einer dieser Ansätze ist das Cross Layer Optimierungs-Paradigma, welches eine bisher nicht mögliche direkte Kommunikation zwischen verteilten Funktionalitäten unterschiedlichen Typs ermöglicht, um ein höheres Maß an Dienstgüte zu erlangen. Ein wesentlicher Indikator, welcher die Relevanz dieses Ansatzes unterstreicht, zeichnet sich durch die Programmierbarkeit von Netzwerkfunktionalitäten aus, welche sich aus der Evolution von heutigen hin zu zukünftigen Netzen erkennen lässt. Dieses Konzept wird als ein vielversprechender Lösungsansatz für Kontrollmechanismen von Diensten in zukünftigen Kernnetzwerken erachtet. Dennoch existiert zur Zeit der Entstehung dieser Doktorarbeit kein Ansatz zur Cross Layer Optimierung in Festnetz-und Mobilfunknetze, welcher der geforderten Effizienz und Flexibilität gerecht wird. Die übergeordnete Zielsetzung dieser Arbeit adressiert die Konzeptionierung, Entwicklung und Evaluierung eines Cross Layer Optimierungsansatzes für Telekommunikationsnetze. Einen wesentlichen Schwerpunkt dieser Arbeit stellt die Definition einer theoretischen Konzeptionierung und deren praktischer Realisierung eines Systems zur Cross Layer Optimierung für Telekommunikationsnetze dar. Die durch diese Doktorarbeit analysierten wissenschaftlichen Fragestellungen betreffen u.a. die Anwendbarkeit von Cross Layer Optimierungsansätzen auf Telekommunikationsnetzwerke; die Betrachtung neuartiger Anforderungen; existierende Konzepte, Ansätze und Lösungen; die Abdeckung neuer Funktionalitäten durch bereits existierende Lösungen; und letztendlich den erkennbaren Mehrwert des neu vorgeschlagenen Konzepts gegenüber den bestehenden Lösungen. Die wissenschaftlichen Beiträge dieser Doktorarbeit lassen sich grob durch vier Säulen skizzieren: Erstens werden der Stand der Wissenschaft und Technik analysiert und bewertet, Anforderungen erhoben und eine Lückenanalyse vorgenommen. Zweitens werden Herausforderungen, Möglichkeiten, Limitierungen und Konzeptionierungsaspekte eines Modells zur Cross Layer Optimierung analysiert und evaluiert. Drittens wird ein konzeptionelles Modell - Generic Adaptive Resource Control (GARC) - spezifiziert, als Prototyp realisiert und ausgiebig validiert. Viertens werden theoretische und praktische Beiträge dieser Doktorarbeit vertiefend analysiert und bewertet.As the telecommunication world moves towards a data-only network environment, signaling, voice and other data are similarly transported as Internet Protocol packets. New requirements, challenges and opportunities are bound to this transition and influence telecommunication architectures accordingly. In this time in which the Internet in general, and telecommunication networks in particular, have entered critical infrastructures and systems, it is of high importance to guarantee efficient and flexible data transport. A certain level of Quality-of-Service (QoS) for critical services is crucial even during overload situations in the access and core network, as these two are the bottlenecks in the network. However, the current telecommunication architecture is rigid and static, which offers very limited flexibility and adaptability. Several concepts on clean slate as well as evolutionary approaches have been proposed and defined in order to cope with these new challenges and requirements. One of these approaches is the Cross Layer Optimization paradigm. This concept omits the strict separation and isolation of the Application-, Control- and Network-Layers as it enables interaction and fosters Cross Layer Optimization among them. One indicator underlying this trend is the programmability of network functions, which emerges clearly during the telecommunication network evolution towards the Future Internet. The concept is regarded as one solution for service control in future mobile core networks. However, no standardized approach for Cross Layer signaling nor optimizations in between the individual layers have been standardized at the time this thesis was written. The main objective of this thesis is the design, implementation and evaluation of a Cross Layer Optimization concept on telecommunication networks. A major emphasis is given to the definition of a theoretical model and its practical realization through the implementation of a Cross Layer network resource optimization system for telecommunication systems. The key questions answered through this thesis are: in which way can the Cross Layer Optimization paradigm be applied on telecommunication networks; which new requirements arise; which of the required functionalities cannot be covered through existing solutions, what other conceptual approaches already exist and finally whether such a new concept is viable. The work presented in this thesis and its contributions can be summarized in four parts: First, a review of related work, a requirement analysis and a gap analysis were performed. Second, challenges, limitations, opportunities and design aspects for specifying an optimization model between application and network layer were formulated. Third, a conceptual model - Generic Adaptive Resource Control (GARC) - was specified and its prototypical implementation was realized. Fourth, the theoretical and practical thesis contributions was validated and evaluated

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

publishedVersio