Coding conventions are ubiquitous in software engineering practice. Maintaining a uniform coding style allows software development teams to communicate through code by making the code clear and, thus, readable and maintainable—two important properties of good code since developers spend the majority of their time maintaining software systems. This dissertation introduces a set of probabilistic machine learning models of source code that learn coding conventions directly from source code written in a mostly conventional style. This alleviates the coding convention enforcement problem, where conventions need to first be formulated clearly into unambiguous rules and then be coded in order to be enforced; a tedious and costly process. First, we introduce the problem of inferring a variable’s name given its usage context and address this problem by creating Naturalize — a machine learning framework that learns to suggest conventional variable names. Two machine learning models, a simple n-gram language model and a specialized neural log-bilinear context model are trained to understand the role and function of each variable and suggest new stylistically consistent variable names. The neural log-bilinear model can even suggest previously unseen names by composing them from subtokens (i.e. sub-components of code identifiers). The suggestions of the models achieve 90% accuracy when suggesting variable names at the top 20% most confident locations, rendering the suggestion system usable in practice. We then turn our attention to the significantly harder method naming problem. Learning to name methods, by looking only at the code tokens within their body, requires a good understating of the semantics of the code contained in a single method. To achieve this, we introduce a novel neural convolutional attention network that learns to generate the name of a method by sequentially predicting its subtokens. This is achieved by focusing on different parts of the code and potentially directly using body (sub)tokens even when they have never been seen before. This model achieves an F1 score of 51% on the top five suggestions when naming methods of real-world open-source projects. Learning about naming code conventions uses the syntactic structure of the code to infer names that implicitly relate to code semantics. However, syntactic similarities and differences obscure code semantics. Therefore, to capture features of semantic operations with machine learning, we need methods that learn semantic continuous logical representations. To achieve this ambitious goal, we focus our investigation on logic and algebraic symbolic expressions and design a neural equivalence network architecture that learns semantic vector representations of expressions in a syntax-driven way, while solely retaining semantics. We show that equivalence networks learn significantly better semantic vector representations compared to other, existing, neural network architectures. Finally, we present an unsupervised machine learning model for mining syntactic and semantic code idioms. Code idioms are conventional “mental chunks” of code that serve a single semantic purpose and are commonly used by practitioners. To achieve this, we employ Bayesian nonparametric inference on tree substitution grammars. We present a wide range of evidence that the resulting syntactic idioms are meaningful, demonstrating that they do indeed recur across software projects and that they occur more frequently in illustrative code examples collected from a Q&A site. These syntactic idioms can be used as a form of automatic documentation of coding practices of a programming language or an API. We also mine semantic loop idioms, i.e. highly abstracted but semantic-preserving idioms of loop operations. We show that semantic idioms provide data-driven guidance during the creation of software engineering tools by mining common semantic patterns, such as candidate refactoring locations. This gives data-based evidence to tool, API and language designers about general, domain and project-specific coding patterns, who instead of relying solely on their intuition, can use semantic idioms to achieve greater coverage of their tool or new API or language feature. We demonstrate this by creating a tool that suggests loop refactorings into functional constructs in LINQ. Semantic loop idioms also provide data-driven evidence for introducing new APIs or programming language features.

Software products have been racing against aging problem for most of their lifecycles, and evolution is the most effective and efficient solution to this problem. Model-Driven Architecture (MDA) is a new technique for software product for evolving development and reengineering methods. The main steps for MDA are to establish models in different levels and phases, therefore to solve the challenges of requirement and technology change. However, there is only a standard established by Object Management Group (OMG) but without a formal method and approach. Presently, MDA is widely researched in both industrial and research areas, however, there is still without a smooth approach to realise it especially in electronic learning (e-learning) system due to the following reasons: (1) models’ transformations are hard to realise because of lack of tools, (2) most of existing mature research results are working for business and government services but not education area, and (3) most of existing model-driven researches are based on Model-Driven Development (MDD) but not MDA because of OMG standard’s preciseness. Hence, it is worth to investigate an MDA-based method and approach to improve the existing software development approach for e-learning system. Due to the features of MDA actuality, a MDA-based evolution method and approach is proposed in this thesis. The fundamental theories of this research are OMG’s MDA standard and education pedagogical knowledge. Unified Modelling Language (UML) and Unified Modelling Language Profile are hired to represent the information of software system from different aspects. This study can be divided into three main parts: MDA-based evolution method and approach research, Platform-Independent Model (PIM) to Platform-Specific Model (PSM) transformation development, and MDA-based electronic learning system evolution. Top-down approach is explored to develop models for e-learning system. A transformation approach is developed to generate Computation Independent Model (CIM), Platform-Independent Model (PIM), and Platform-Specific Model (PSM); while a set of transformation rules are defined following MDA standard to support PSM’ s generation. In addition, proposed method is applied in an e-learning system as a case study with the prototype rules support. In the end, conclusions are drawn based on analysis and further research directions are discussed as well. The kernel contributions are the proposed transformation rules and its application in electronic learning system.

Knowledge is one of the most important assets of an organization that directly affects business success, and its importance increases for organizations that use knowledge-intensive processes such as the software development industry. In an industry in which technological developments are rapid, in order to keep up with the continuously increasing competition and to obtain competitive advantage the software organizations need to obtain the correct knowledge, use it efficiently and pass it to future projects evolving it accordingly. The major aim of this research is to propose a novel model, namely AiOLoS, for assessing the level of organizational learning and learning characteristics in software development organizations. The primary contributions of this two-legged AiOLoS model are the identification of the major process areas and the core processes that a learning software organization follows during its organizational learning process and to provide the necessary measures and metrics and the corresponding definitions/interpretations for the assessment of the learning characteristics of the software development organization. The research is supported with a multiple case-study work conducted in software development teams in order to identify the mapping of the core processes and the applicability of the AiOLoS model to software development organizations, its utilization as a tool for assessing organizational learning and providing a basis for software process improvement.

Na engenharia de software há os modelos tradicionais e os modelos ágeis. Porém, não há um consenso sobre quais são as práticas necessárias para se obter sucesso em um projeto de engenharia de software. O presente trabalho propõe a análise do modelo SEMAT através de um estudo de caso usando esse modelo para a concepção e o levantamento de requisitos de um software para e-learning, na área de ensino de banco de dados. A partir desse estudo de caso foi possível verificar a aplicabilidade do modelo para a aplicação, além de demonstrar algumas restrições e adaptações necessárias para o uso nessa área específica. / In software engineering there are traditional and agile models. However, there is no consensus on what practices are required for a successful software engineering project. This work proposes to analyze the SEMAT model through a study case for the conception and requirements elicitation of an e-learning database education software. From this study case it was possible to verify the model applicability for the application, as well as to understand some restrictions and adjustments required for this specific area.

Na engenharia de software há os modelos tradicionais e os modelos ágeis. Porém, não há um consenso sobre quais são as práticas necessárias para se obter sucesso em um projeto de engenharia de software. O presente trabalho propõe a análise do modelo SEMAT através de um estudo de caso usando esse modelo para a concepção e o levantamento de requisitos de um software para e-learning, na área de ensino de banco de dados. A partir desse estudo de caso foi possível verificar a aplicabilidade do modelo para a aplicação, além de demonstrar algumas restrições e adaptações necessárias para o uso nessa área específica. / In software engineering there are traditional and agile models. However, there is no consensus on what practices are required for a successful software engineering project. This work proposes to analyze the SEMAT model through a study case for the conception and requirements elicitation of an e-learning database education software. From this study case it was possible to verify the model applicability for the application, as well as to understand some restrictions and adjustments required for this specific area.

As a part of the quality management, product defectiveness prediction is vital for small software organizations as for instutional ones. Although for defect prediction there have been conducted a lot of studies, process enactment data cannot be used because of the difficulty of collection. Additionally, there is no proposed approach known in general for the analysis of process enactment data in software engineering. In this study, we developed a method to show the applicability of process enactment data for defect prediction and answered &ldquo / Is process enactment data beneficial for defect prediction?&rdquo / , &ldquo / How can we use process enactment data?&rdquo / and &ldquo / Which approaches and analysis methods can our method support?&rdquo / questions. We used multiple case study design and conducted case studies including with and without process enactment data in a small software development company. We preferred machine learning approaches rather than statistical ones, in order to cluster the data which includes process enactment informationsince we believed that they are convenient with the pattern oriented nature of the data. By the case studies performed, we obtained promising results. We evaluated performance values of prediction models to demonstrate the advantage of using process enactment data for the prediction of defect open duration value. When we have enough data points to apply machine learning methods and the data can be clusteredhomogeneously, we observed approximately 3% (ranging from -10% to %17) more accurate results from analyses including with process enactment data than the without ones. Keywords:

With rapid development of science and technology, introduction of the ICT different methodologies into the learning environment today becomes one of the most important factors. Application of IT tools in classroom learning in and methodology for teaching and learning processes creates number of issues, which could be solved with the help of online Learning Management System (LMS). This paper presents experiment results using of Moodle, at the course of Linear algebra and analytic geometry (LAAG) in the first semester of 2010-2011 and 2011-2012 study year. The paper presents quantitative and qualitative rationale interdependence analysis and experiment conclusion based on midterm and final exam results of the freshman students of the National University of Mongolia.

This master's thesis is focused on Information Technology Infrastructure Library (ITIL). Objective of the project was to analyze, design and implement a game engine, which would provide simulation of IT service operation in real or accelerated time or in turns. Basic part of the engine is a creators mode, which allows users to create custom IT services and specify their behaviour during operation, like the service would be used in real. Another part of the engine is a players mode and simple service desk. In this mode, players can take care of fluent operation of their services. Thanks to this, they can learn and train practices, which are described in ITIL.

This master's thesis presents analysis, design and implementation of an information system for supporting studying and practicing of selected methods described by Information Technology Infrastructure Library (ITIL) with focus on service design and service operation. In the form of a web application the thesis presents an uncommon approach to e-learning system development by extending the standard lecture part by game elements and interactive scenarios. Developed system allows creators to define services and tasks, such as incidents and problems caused by their operation, and evaluate reactions of responsible users. The application can be used by IT administrators or Service Desk managers to train new IT engineers or Service Desk operators.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-09-22T18:42:19Z No. of bitstreams: 1 AdrianaCosta.pdf: 3838428 bytes, checksum: 2c98d9d837cb8ba187aa038e80b2d304 (MD5) / Made available in DSpace on 2017-09-22T18:42:19Z (GMT). No. of bitstreams: 1 AdrianaCosta.pdf: 3838428 bytes, checksum: 2c98d9d837cb8ba187aa038e80b2d304 (MD5) Previous issue date: 2017-08-14 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão / Software agents represent an evolution of traditional software entities, having the ability to control their own behavior and acting with autonomy. Typically, software agents act reactively, where actions and perceptions are predefined at design time, or in a deliberative way, where the corresponding action for a given perception is found at run time through reasoning. Deliberative agents do not need all knowledge to be predefined; on the contrary, from an initial knowledge they can infer new knowledge. However, to find an action appropriate to a particular perception, they take a long time, generating a high computational cost. As a solution to this problem, a hybrid architecture with learning for the development of hybrid software agents is presented in this work. Hybrid agents combine reactive and deliberative behavior taking advantage of the speed of reactive behavior and the reasoning capability of the deliberative one are a better option for structuring software agents. The main advantages of the proposed architecture are learning of the reactive behavior, faster and more efficient, through the interactions of the agent with its environment and its consequent adaptability to the environment. The agent adapts to the environment as it learns new reactive behavior from frequent deliberative behavior. The proposed architecture was evaluated through the development of case studies in the information security domain using case-based reasoning, ontologies for the representation of domain knowledge and supervised learning for automatic generation of reactive rules. Results obtained with the case studies performed confirmed a greater effectiveness and a shorter response time of the hybrid agent with learning regarding both the reactive or deliberative agent as well as a hybrid agent without learning in the intrusion detection in computer networks domain. From the specification and evaluation of the hybrid architecture with supervised learning in the Information Security domain, a reference architecture for the development of hybrid agents with learning was generalized. In future works, we intend to evaluate this reference architecture in other domains, with other types of reasoning and learning techniques to evaluate its impact on the productivity and quality of the development of hybrid software agents. / Os agentes de software representam uma evolução do software tradicional, tendo a capacidade de controlar seu próprio comportamento e agir com autonomia. Tipicamente, os agentes de software agem de forma reativa, onde as percepções e ações são predefinidas no momento da sua concepção, ou de forma deliberativa, onde a ação correspondente para uma determinada percepção é encontrada em tempo de execução através de um processo de raciocínio. Os agentes deliberativos não necessitam que todo o conhecimento seja predefinido, ao contrário, a partir de um conhecimento inicial eles conseguem inferir novo conhecimento. Todavia, em muitos casos, para encontrar uma ação apropriada a uma determinada percepção eles levam muito tempo, gerando um alto custo computacional. Como solução a esse problema, apresentamos neste trabalho uma arquitetura híbrida com aprendizagem para o desenvolvimento de agentes de software híbridos. Os agentes híbridos, que combinam comportamento reativo e deliberativo, são uma opção melhor para estruturar os agentes de software. As principais vantagens da arquitetura tese é o aprendizado do comportamento reativo, mais rápido e eficiente, através de interações do agente com o seu ambiente e a sua consequente adaptabilidade ao ambiente. O agente se adapta ao ambiente na medida em que aprende novo comportamento reativo a partir de comportamento deliberativo frequente. A arquitetura tese foi avaliada através do desenvolvimento de estudos de casos no domínio da segurança da informação utilizando o raciocínio baseado em casos, ontologias para a representação do conhecimento do domínio de estudo e aprendizagem supervisionada para geração automática de regras reativas. Os resultados obtidos com os estudos de casos realizados confirmaram uma efetividade maior e um menor tempo de resposta do agente híbrido com aprendizagem em relação tanto ao comportamento isolado de um agente reativo ou deliberativo bem como de um agente híbrido sem aprendizagem no domínio da detecção de intrusões em redes de computadores. A partir da especificação e avaliação da arquitetura híbrida com aprendizagem supervisionada no domínio da Segurança da Informação, foi generalizada uma arquitetura de referência para o desenvolvimento de agentes híbridos com aprendizagem. Em trabalhos futuros, pretende-se avaliar esta arquitetura de referência em outros domínios, com outros tipos de raciocínio e técnicas de aprendizagem para avaliar o seu impacto na produtividade e qualidade do desenvolvimento de agentes de software híbridos.