The aim of this work is to propose such a methodology for designing of SW applications architecture that allows effortless, exact and systematical transformation of the real business process into a model suitable as an assignment for implementation by programmers. The aim of this proposal is such a methodology that would allow to reach the above mentioned goals on the basis of easy-to-understand and simple principles and that would be available either as a fundament for its usage within methodologies that are focused on the management of such projects, or as an alternative to methodologies that are much to expensive, complex and/or designed for very large development teams.

This thesis deals with methods of project management, including the evaluation and designing of a custom solution for small development teams with frequent teleworking. The thesis also compares the particular difference between classical predictive and agile software development methods. Based on a theoretical background and current state analysis, the purpose of this work is to design a new information system, which should serve as a SaaS solution, and describe its implementation by the proposed method.

Diplomová práce je zaměřena na stanovení priority testovacích případů s využitím fuzzy logiky. Vhodným přístupem k získání výstupu na základě definovaného vstupu a stanovených pravidel byl zvolen fuzzy model přiřazující prioritu testovacím případům. K dosažení cíle práce byla nejprve stanovena kritéria, parametry a poté určena jejich váha pro jednotlivé testovací případy. Na závěr jsou vyhodnocena vstupní data s využitím řešení v programu MS Excel a MATLAB.

This diploma thesis deals with the issue of information transfer between the Department of Electrical Equipment Design and the Software Development Department of the control system of this machine. The diploma thesis focuses mainly on the elimination of the influence of the human factor while increasing the efficiency of this information transfer. System analysis examines the issues and investigates the reliability of information transfer. On the basis of the requirements resulting from the analysis, preventive measures and modifications of procedures in both departments have been proposed. Simultaneously, its own software applications have been developed to considerably simplify and accelerate the process, while meeting the requirements to eliminate problematic phenomena caused particularly by human factors. The resulting solution is verified according to the designated verification process and reassessed by own "SampleVUT" test project. Validation evaluates the effectiveness of the proposed solution.

Les protéines sont omniprésentes dans les processus biologiques. Identifier leurs fonctions aide à comprendre et éventuellement à contrôler ces processus. Cependant, si la détermination de la séquence protéique est désormais une procédure de routine, il est souvent difficile d'utiliser cette information pour extraire des connaissances fonctionnelles pertinentes sur le système étudié. En effet, la fonction d'une protéine repose sur ses propriétés chimiques et mécaniques, lesquelles sont définies par sa structure. Ainsi, la prédiction, la compréhension et l'analyse de la structure des protéines sont parmi les principaux défis de la biologie moléculaire.La prédiction et l'analyse des repliements de protéines est le sujet central de cette thèse. Cependant, de nombreuses protéines sont organisées selon des assemblages qui sont symétriques dans la plupart des cas et certaines protéines contiennent des répétitions internes. La conception d'une structure avec des répétitions ou d'un assemblage protéique symétrique est souvent le moyen le plus simple pour l'évolution d'atteindre une certaine fonction. Ceci qui nous a poussé à développer des méthodes spécialement conçues pour les assemblages protéiques symétriques et les protéines avec répétitions internes. Une autre motivation derrière cette thèse était d'explorer et de faire progresser le domaine émergent de l'apprentissage profond appliqué aux données atomistiques tridimensionnelle (3D).Cette thèse s'articule autour de deux parties. Dans la première partie, nous proposons des algorithmes pour analyser la structures des assemblages symétriques de protéines. Nous commençons par définir une mesure de symétrie basée sur la distance euclidienne 3D et décrivons un algorithme permettant de calculer efficacement cette mesure et de déterminer les axes de symétrie des assemblages protéiques. Cet algorithme est capable de traiter tous les groupes ponctuels de symétrie, à savoir les symétries cycliques, dièdrales, tétraédriques, octaédriques et icosaédriques, grâce à une heuristique robuste qui perçoit la correspondance entre sous-unités asymétriques. Nous étendons ensuite les limites du problème et proposons une méthode applicable à des cartes de densité 3D. Nous abordons ce problème à l'aide d'un réseau neuronal profond (DNN), et nous proposons une méthode qui prédit l'ordre de symétrie l'axe de symétrie 3D.Ensuite, nous proposons une architecture DNN pour évaluer la qualité de modèles 3D de repliements de protéines. Nous avons entrainé le DNN en utilisant en entrée la géométrie locale autour de chaque résidu dans un modèle de protéine représenté par une carte de densité, et avons prédit les CAD-scores de ces résidus. Le DNN a été créé pour être invariant par rapport à l'orientation du modèle d'entrée. Nous avons également conçu certaines parties du DNN pour reconnaître automatiquement les propriétés des atomes et sélectionner des descripteurs pertinents. Enfin, nous analysons les descripteurs appris par le DNN. Nous montrons que notre architecture apprend effectivement des propriétés des atomes, des acides aminés et des structures moléculaires de niveau supérieur. Certaines propriétés sont déjà bien étudiées comme les éléments chimiques, les charges partielles atomiques, les propriétés des acides aminés, la structure secondaire des protéines et l'exposition au solvant. Nous démontrons également que notre réseau apprend de nouvelles caractéristiques structurelles.Cette étude présente de nouveaux outils pour la biologie structurale. Certains sont déjà utilisés dans la communauté, par les évaluateurs de CASP par example. Elle démontre également la puissance de l'apprentissage profond pour la représentation de la structure des protéines et son applicabilité aux problèmes des données 3D. / Proteins are ubiquitous for virtually all biological processes. Identifying their role helps to understand and potentially control these processes. However, even though protein sequence determination is now a routine procedure, it is often very difficult to use this information to extract relevant functional knowledge about system under study. Indeed, the function of a protein relies on a combination of its chemical and mechanical properties, which are defined by its structure. Thus, understanding, analysis and prediction of protein structure are the key challenges in molecular biology.Prediction and analysis of individual protein folds is the central topic of this thesis. However, many proteins are organized in higher-level assemblies, which are symmetric in most of the cases, and also some proteins contain internal repetitions.In many cases, designing a fold with repetitions or designing a symmetric protein assembly is the simplest way for evolution to achieve a specific function. This is because the number of combinatorial possibilities in the interactions of designed folds reduces exponentially in the symmetric cases. This motivated us to develop specific methods for symmetric protein assemblies and also for individual proteins with internal repeats. Another motivation behind this thesis was to explore and advance the emerging deep neural network field in application to atomistic 3-dimensional (3D) data.This thesis can be logically split into two parts. In the first part, we propose algorithms to analyse structures of protein assemblies, and more specifically putative structural symmetries.We start with a definition of a symmetry measure based on 3D Euclidean distance, and describe an algorithm to efficiently compute this measure, and to determine the axes of symmetry of protein assemblies. This algorithm is able to deal with all point groups, which include cyclic, dihedral, tetrahedral, octahedral and icosahedral symmetries, thanks to a robust heuristic that perceives correspondence between asymmetric subunits. We then extend the boundaries of the problem, and propose a method applicable to the atomistic structures without atom correspondence, internal symmetries, and repetitions in raw density maps. We tackle this problem using a deep neural network (DNN), and we propose a method that predicts the symmetry order and a 3D symmetry axis.Then, we extend the DNN architecture to recognise folding quality of 3D protein models. We trained the DNN using as input the local geometry around each residue in a protein model represented as a density map, and we predicted the CAD-scores of these residues. The DNN was specifically conceived to be invariant with respect to the orientation of the input model. We also designed some parts of the network to automatically recognise atom properties and robustly select features. Finally, we provide an analysis of the features learned by the DNN. We show that our architecture correctly learns atomic, amino acid, and also higher-level molecular descriptors. Some of them are rather complex, but well understood from the biophysical point of view. These include atom partial charges, atom chemical elements, properties of amino acids, protein secondary structure and atom solvent exposure. We also demonstrate that our network learns novel structural features.This study introduces novel tools for structural biology. Some of them are already used in the community, for example, by the PDBe database and CASP assessors. It also demonstrates the power of deep learning in the representation of protein structure and shows applicability of DNNs to computational tasks that involve 3D data.

Traceability data provides the knowledge on dependencies and logical relations existing amongst artefacts that are created during software development. In reasoning over traceability data, conclusions can be drawn to increase the quality of software. The paradigm of Model-driven Software Engineering (MDSD) promotes the generation of software out of models. The latter are specified through different modelling languages. In subsequent model transformations, these models are used to generate programming code automatically. Traceability data of the involved artefacts in a MDSD process can be used to increase the software quality in providing the necessary knowledge as described above. Existing traceability solutions in MDSD are based on the integral model mapping of transformation execution to generate traceability data. Yet, these solutions still entail a wide range of open challenges. One challenge is that the collected traceability data does not adhere to a unified formal definition, which leads to poorly integrated traceability data. This aggravates the reasoning over traceability data. Furthermore, these traceability solutions all depend on the existence of a transformation engine. However, not in all cases pertaining to MDSD can a transformation engine be accessed, while taking into account proprietary transformation engines, or manually implemented transformations. In these cases it is not possible to instrument the transformation engine for the sake of generating traceability data, resulting in a lack of traceability data. In this work, we address these shortcomings. In doing so, we propose a generic traceability framework for augmenting arbitrary transformation approaches with a traceability mechanism. To integrate traceability data from different transformation approaches, our approach features a methodology for augmentation possibilities based on a design pattern. The design pattern supplies the engineer with recommendations for designing the traceability mechanism and for modelling traceability data. Additionally, to provide a traceability mechanism for inaccessible transformation engines, we leverage parallel model matching to generate traceability data for arbitrary source and target models. This approach is based on a language-agnostic concept of three similarity measures for matching. To realise the similarity measures, we exploit metamodel matching techniques for graph-based model matching. Finally, we evaluate our approach according to a set of transformations from an SAP business application and the domain of MDSD.

Masters of Science / In a world where technology is advancing at a very rapid pace, global competition has significantly increased, and this is putting pressure on software companies to produce quality software. It has therefore become critically important to manage well the implementation of software engineering projects by employing effective methods that ensure the best product is produced. The most popular software project implementation methodologies are the Traditional methods and Agile methods. This research explored these two methodologies by comparing the strength and weakness of both approaches. The research was conducted using a constructionist epistemology with a critical inquiry using the grounded theory methodology, applying both quantitative and qualitative methods to the case studies. Findings were collected through participant observation using a designed questionnaire targeting a selected sample of the study population. This sample of the population consisted of Ecommerce organizations in the Western Cape province of South Africa to establish which of the Traditional or Agile methods would best lead to the successful implementation of Ecommerce software engineering projects. The research results showed that the Agile methodology was the preferred and recommended approach. Very few participants of the research supported the Traditional approach to still be considered and used for projects with well-known end goals. An Ecommerce website prototype for a local Cape Town business was constructed as following the Agile approach to measure and validate the findings of the research. The prototype was built successfully from conception to the final delivery product and on time confirming the Agile approach as best for Ecommerce software development. In conclusion, the Agile methodology is the choice approach based on reviewed literature, the research results, and the prototype construction. These results will help in critical decision making regarding an appropriate development methodology to follow for the Ecommerce industry in the Western Cape.

Mob programming is a collaborative software development method that has gained increasing attention in both industry and research. While the focus of mob programming is on the benefits of teams programming together, there are also potential benefits for other aspects of the software development process. However, there is a lack of research on the use of the method outside the domain of programming. This study explores user-centred design (UCD) in mob programming through a case study of three software development teams at Sveriges Television, a Swedish public broadcasting company. Results show that the teams use the method for a variety of tasks in their daily work, calling for a rebranding of the method to mob development to encompass the broader scope. The integration of UCD is analysed through the principles of user-centred agile software development. The results indicate that a revision of these principles is needed to include the cross-functional and social factors that mob development adds to the software development process.

Agile software development methods have seen great success in software teams. Research on the topic of adopting agile methods in development teams is extensive. In the literature key enabling factors are identified and numerous benefits of agile ways of working are named. Less attention has been payed to the non-software functions in software development organizations, though. Moreover, little is known about how well the enabling factors and benefits for software teams translate to other teams in the organization. The goal of this study is to evaluate what benefits agile methods provide to non-software teams, whether the enabling factors are similar and what the challenges and drawbacks for adopting agile methods in commercial teams are. Using the case of the Swedish Software-as-a-Service company Funnel, which introduced agile practices into their commercial teams, these questions are tackled. The study finds that knowledge transfer and governance are core areas that need to be engaged in during the adoption process. With decisions being made more autonomously ensuring the exchange of relevant information is crucial. The autonomy creates new demands for the governance structure, making a guiding vision and clear strategic direction crucial for individuals to remain capable of acting. An additional focus is laid on the interplay of organizational values and agile methods. The study concludes that the introduction of agile methods to commercial teams is beneficial for the organization and helps teams solve more complex problems. It further argues that the distinction drawn between agile practices and agile enablers is misleading because of the reciprocal dependence. Finally, it is argued, that the distinction of benefits and drawbacks arising from agile methods favors agile adoption as an end in itself. The actual benefit of adopting agile practices may lie with how it makes an organization practice change, engage proactively with organizational challenges and, as a consequence, develop a greater exaptive potential.

This report evaluates the team-structure of three software maintenance teams in order to decide their level of featureness (a term that defines to what extent a team has the quality (the set of characteristics) of being a feature team). Simulations of changes that are expressed as beneficial in an agile environment and that could increase the teams‘ level of featureness within the team structure are performed. The results show that each team‘s level of featureness is affected differently by each change. Partly, this underlines the importance of understanding the current team-structure before implementing changes that aim to increase the level of featureness. And secondly, within the scope of the study, the change where a user expert is declared a team member is concluded as the change that increases the teams‘ level of featureness the most. Based on the results the report also concludes that it is essential to implement changes that affect different, which in combination can increase the level of featureness.