Os sistemas de modelagem de distribuição provável de espécie são utilizados para identificar regiões onde há maior probabilidade de uma determinada espécie viver, baseado nas características ambientais de localizações onde é conhecido que há presença daquela espécie. Estes sistemas requerem computação de alto desempenho devido à grande quantidade de dados que precisam ser processados para se gerar um modelo com a distribuição provável. Um momento importante do processo de modelagem de distribuição de espécies é a coleta dos dados, no qual os pesquisadores vão a campo para identificar alguns pontos de presença ou ausência, no entanto este processo é feito de forma ainda manual. Este trabalho apresenta uma proposta de automação deste processo por meio da utilização de dispositivos móveis e arquitetura SOA. Historicamente, os sistemas que utilizam arquiteturas orientadas a serviço não consideram fortemente as limitações inerentes aos dispositivos móveis, tais como poder de processamento, capacidade de armazenamento e duração da bateria. A falta de uma infra-estrutura de referência para estes sistemas pode ser apontada como uma das causas deste problema. Assim, é proposta uma infra-estrutura de desenvolvimento para sistemas móveis que utilizam uma arquitetura orientada a serviços voltada à experiência do usuário. Esta infra-estrutura tem especial atenção a como as restrições dos sistemas móveis influenciam tanto a arquitetura de software quanto a apresentação dos serviços para o usuário final. Disserta-se sobre fatores e questões relevantes ao projeto de sistemas de natureza móvel sugerindo a sua consideração durante a elaboração de projetos semelhantes. São propostos também um conjunto de métricas para avaliação do desempenho para a realização de experimentos com o objetivo de validar os aspectos de usabilidade e arquitetura, além de identificar modificações na infra-estrutura proposta. / Species distribution modeling system are used to identify regions where there is the probability to a species survive, based on the environmental characteristics where there is for sure presence of a species. Those systems require high performance computation due the large amount of data that are processed in order to create the distribution model. An important moment of the modeling process is the data collecting, when the researchers go field to identify the localizations of presence or absence, but this process is still done manually. This paper presents a proposal for automating this process through the use of mobile devices and SOA architecture. Historically, systems using service oriented architectures do not consider strongly the limitations of mobile devices such as processing power, storage capacity and duration of battery. The lack of an infrastructure of reference for these systems can be considered one of the causes of this problem. Thus, we propose an infrastructure development for mobile systems using a service-oriented architecture focused on user experience. This infrastructure has special attention to the restrictions of mobile systems influence both the software architecture as the presentation of services to the end user. Mobile related issues are discussed suggesting its consideration during the development of similar projects. Also, it is proposed a set of metrics for performance evaluation to carry out experiments aimed at validating the architecture and usability aspects, and identify changes in the proposed infrastructure.

The notion of maximizing software reuse among the family of products has gained considerable attention in the last decade. Lots of research has been done on designing and managing the commonalities and variabilities between the products. However, very few metrics have been developed to assist architects in designing product line architectures. The structure of the product line holds immense importance towards increasing the life span of the product line. Since many of the product line architecture design methodologies follow a component based approach, it seems logical to attempt to adapt the component based metrics to the product line domain. In this thesis, we attempt to derive metrics that quantify the structural quality of product line architecture. / +92-42-5727639

Capturing the quality of software and detecting sections for further scrutiny within are of high interest for industry as well as for education. Project managers request quality reports in order to evaluate the current status and to initiate appropriate improvement actions and teachers feel the need of detecting students which need extra attention and help in certain programming aspects. By means of software measurement software characteristics can be quantified and the produced measures analyzed to gain an understanding about the underlying software quality. In this study, the technique of code profiling (being the activity of creating a summary of distinctive characteristics of software code) was inspected, formulized and conducted by means of a sample group of 19 industry and 37 student programs. When software projects are analyzed by means of software measurements, a considerable amount of data is produced. The task is to organize the data and draw meaningful information from the measures produced, quickly and without high expenses. The results of this study indicated that code profiling can be a useful technique for quick program comparisons and continuous quality observations with several application scenarios in both industry and education.

Following the success of software engineering design patterns, security patterns are a promising approach to aid in the design and development of more secure software systems. At the same time, recent work on aspect-oriented programming (AOP) suggests that the cross-cutting nature of software security concerns makes it a good candidate for AOP techniques. This work uses a set of software metrics to evaluate and compare object-oriented and aspect-oriented implementations of five security patterns--Secure Base Action, Intercepting Validator, Authentication Enforcer, Authorization Enforcer, and Secure Logger. Results show that complete separation of concerns was achieved with the aspect-oriented implementations and the modularity of the base application was improved, but at a cost of increased complexity in the security pattern code. In most cases the cohesion, coupling, and size metrics were improved for the base application but worsened for the security pattern package. Furthermore, a partial aspect-oriented solution, where the pattern code is decoupled from the base application but not completely encapsulated by the aspect, demonstrated better modularity and reusability than a full aspect solution. This study makes several contributions to the fields of aspect-oriented programming and security patterns. It presents quantitative evidence of the effect of aspectization on the modularity of security pattern implementations. It augments four existing security pattern descriptions with aspect-oriented solution strategies, complete with new class and sequence diagrams based on proposed aspect-oriented UML extensions. Finally, it provides a set of role-based refactoring instructions for each security pattern, along with a proposal for three new basic generalization refactorings for aspects.

La plupart des métriques de qualité logicielle mesurables sont actuellement basées sur des mesures bas niveau, telles que la complexité cyclomatique, le nombre de lignes de commentaires ou le nombre de blocs dupliqués. De même, la qualité de l'ingénierie logicielle est davantage liée à des facteurs techniques ou de gestion, et devrait fournir des indicateurs utiles pour les exigences de qualité. Actuellement, l'évaluation de ces exigences de qualité n'est pas automatisée, elle n'est pas validée empiriquement dans des contextes réels et l'évaluation est définie sans tenir compte des principes de la théorie de la mesure. Par conséquent, il est difficile de comprendre où et comment améliorer le logiciel suivant le résultat obtenu. Dans ce domaine, les principaux défis consistent à définir des métriques adéquates et utiles pour les exigences de qualité, les documents de conception de logiciels et autres artefacts logiciels, y compris les activités de test.Les principales problématiques scientifiques abordées dans cette thèse sont les suivantes: définir des mesures et des outils de support pour mesurer les activités d'ingénierie logicielle modernes en termes d'efficacité et de qualité. La seconde consiste à analyser les résultats de mesure pour identifier quoi et comment s'améliorer automatiquement. Le dernier consiste en l'automatisation du processus de mesure afin de réduire le temps de développement. Une telle solution hautement automatisée et facile à déployer constituera une solution révolutionnaire, car les outils actuels ne le prennent pas en charge, sauf pour une portée très limitée. / Most of the measurable software quality metrics are currently based on low level metrics, such as cyclomatic complexity, number of comment lines or number of duplicated blocks. Likewise, quality of software engineering is more related to technical or management factoid, and should provide useful metrics for quality requirements. Currently the assessment of these quality requirements is not automated, not empirically validated in real contexts, and the assessment is defined without considering principles of measurement theory. Therefore it is difficult to understand where and how to improve the software following the obtained result. In this domain, the main challenges are to define adequate and useful metrics for quality requirements, software design documents and other software artifacts, including testing activities.The main scientific problematic that are tackled in this proposed thesis are the following : defining metrics and its supporting tools for measuring modern software engineering activities with respect to efficiency and quality. The second consists in analyzing measurement results for identifying what and how to improve automatically. The last one consists in the measurement process automation in order to reduce the development time. Such highly automated and easy to deploy solution will be a breakthrough solution, as current tools do not support it except for very limited scope.

The main purpose of exception handling mechanisms is to improve software robustness by handling exceptions when they occur. However, empirical evidence indicates that improper implementation of exception handling code can be a source of faults in software systems. There is still limited empirical knowledge about the relationship between exception handling code and defects. In this dissertation, we present three case studies investigating defect densities of exception handling code. The results show that in every system under study, the defect density of exception handling code was significantly higher than the defect density of overall source code and normal code. The ability to predict the location of faults can assist in directing quality enhancement efforts to modules that are likely to have faults. This information can be used to guide test plans, narrow the test space, and improve software quality. We hypothesize that complicated exception handling structure is a predictive factor that is associated with defects. To the best of our knowledge, no study has addressed the relationship between the attributes of exception handling method call structures and defect occurrence, nor has prior work addressed fault prediction. We extract exception-based software metrics from the structural attributes of exception handling call graphs. To find out whether there are patterns of relationship between exception-based software metrics and fault-proneness, we propose a defect prediction model using exception handling call structures. We apply the J48 algorithm, which is the Java implementation of the C4.5 algorithm, to build exception defect prediction models. In two out of three systems under study, the results reveal that there are logical patterns of relationships between most class level exception metrics and fault-proneness. The accuracy of our prediction models is comparable to the results of defect prediction model studies in the literature. It was observed that our approach has somewhat worse predictive accuracy when a system has low average defects per class.

In recent years, there has been a surge in research on theimpact of architectural smells on software maintainability.Maintainability in turn encompasses several other qualityattributes as sub-characteristics, such as modularity andtestability. However, the empirical evidence establishing aclear relationship between these quality attributes andarchitectural smells has been lacking. This study aims to fillthis gap by examining the correlation between sevenarchitectural smells and testability/modularity across 378versions of eight open-source projects. A self-developedtool—ASAT—was used to collect data on architecturalsmells and metrics relating to modularity and testability. Thecollected data was analyzed to reveal correlations at both theproject-level and within packages. Contrary to expectations,the findings show that, generally, there is no negativecorrelation between smells and modularity at the projectlevel, except for the Dense Structure smell. Remarkably,project-level testability showed the opposite result.However, a rival explanation proposes that the increasingsize of a project may be a stronger factor in this relationship.Similarly, package-level smells, as a whole, did not exhibit anegative correlation with testability. However, most smellsdemonstrated a stronger negative relationship with thequality attributes they were claimed to impair, incomparison to their counterparts. This empirical evidencesubstantiates the assertion that specific architectural smellsindeed relate to distinct quality attributes, which hadpreviously only been supported by argument.

[pt] Desenvolvedores realizam mudanças de código constantemente durante a vida de um projeto de software. Essas mudanças podem induzir a degradação progressiva do design. A degradação do design pode ser reduzida ou acelerada por fatores que interagem em cada mudança. Esses fatores podem variar desde uma mudança ou ação de reparo específica – e.g., refatorações – até a maneira como os desenvolvedores contribuem e discutem mudanças. Entretanto, estudos anteriores não exploram como esses fatores interagem e influenciam na degradação do design. Eles apenas focam em alguns fatores e tendem a os investigar em isolamento. Estudar os fatores em isolamento pode não explicar adequadamente qual é o conjunto mais relevante de interações entre fatores e qual sua influência na degradação do design. Isso pode indicar que abordagens existentes para evitar ou mitigar a degradação do design são incompletas, já que elas não consideram interações entre fatores que podem ser relevantes. Portanto, essa dissertação relata uma investigação que almeja aumentar a compreensão sobre como uma ampla gama de interações entre fatores pode afetar a degradação do design, para que consequentemente possam ser investigadas práticas efetivas para evitar ou mitigar esse fenômeno. Para tal fim, nós realizamos uma análise aprofundada buscando preencher lacunas no conhecimento existente sobre dois tipos de fatores: fatores relacionados ao processo (i.e. relacionados às mudanças e seus resultados produzidos) e fatores relacionados ao desenvolvedor (i.e. relacionados ao desenvolvedor trabalhando nas mudanças). Nós focamos em analisar os efeitos de possíveis interações entre os fatores previamente mencionados e uma série de sub-fatores, no que diz respeito como essas interações afetam módulos que sofreram diferentes níveis de degradação. Por exemplo, nós observamos que: (1) individualmente, tanto o sub-fator relacionado ao desenvolvedor que representa um desenvolvedor novato (que está contribuindo pela primeira vez), quanto o sub-fator relacionado ao processo que representa tamanho de uma mudança, não se mostraram relacionados a efeitos negativos na qualidade de código das classes alteradas. Porém, analisando interações entre fatores, nós observamos que mudanças em que esses dois fatores interagem tendem a ter um efeito negativo no código, causando degradação. Interessantemente, esse comportamento não se alterou mesmo quando mudança foi introduzida através de uma pull request (o que frequentemente inicia um processo de revisão de código), (2) surpreendentemente, refatorações de extração frequentemente não tem um efeito positivo na qualidade do código, enquanto, em contrapartida, as refatorações de movimentação foram predominantemente positivas. Nós também discutimos como esses achados apresentados na dissertação podem ajudar desenvolvedores e pesquisadores na melhoria de suas diretrizes sobre como evitar e monitorar a degradação do design. / [en] Developers constantly perform code changes throughout the lifetime of a project. These changes may induce the introduction of design decay over time. Design decay may be reduced or accelerated by interacting factors that underlie each change. These factors may come from specific actions of change or repair – e.g., refactorings – to how developers contribute and discuss the changes. However, existing studies do not explain how these factors interact and influence design decay. They solely tend to focus on a few types of factors, and often consider them in isolation. Interactions between factors may cause different outcomes than those previously studied. Studying factors in isolation may not properly explain what are the most relevant set of interacting factors that influence design decay. This may indicate that existing approaches to avoid or mitigate design decay are misleading since they do not consider potentially relevant interactions between various factors. Thus, this dissertation reports an investigation that aims to increase the understanding of how a wide range of interacting factors can influence design decay in order to facilitate the investigation of which practices can be used to avoid or mitigate design decay. To this end, we performed an in-depth analysis to fill knowledge gaps on two types of factors: process-related (i.e., related to changes and their produced outcomes) and developer-related (i.e., related to the developer working on the changes) factors. We focused on analyzing the effects of potential interactions between the aforementioned factors and 12 sub-factors with regards to how they affected modules with different levels of decay. We observed diverging decay patterns in these modules. Our results indicate that both types of factors can be used to distinguish between different decay levels in classes. We have also observed that: (1) individually, the developer-related subfactor that represented first-time contributors, as well as the process-related one that represented size of the changes, did not exert negative effects on the changed classes. However, when analyzing specific factor interactions, we saw that changes where both of these factors interacted tended to have a negative effect and led to decay. Interestingly, this behaviour did not alter even when the change was introduced via pull request (which usually triggers a code review process); (2) surprisingly, extraction-type refactorings often do not have a positive effect on code quality, while, by contrast, move refactorings were mostly positive. We also discuss how these findings in this dissertation can aid developers and researchers in improving their guidelines for the avoidance and monitoring of design decay.

Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products.

Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security at the early stage of development using traceable patterns and software metrics. The concept of traceable patterns is similar to design patterns, but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared to the traditional software metrics, they can be used in developing a vulnerability prediction model to classify code as vulnerable or not. By analyzing and comparing the performance of traceable patterns with metrics, we propose a vulnerability prediction model. Objective: This study explores the performance of code patterns in vulnerability prediction and compares them with traditional software metrics. We have used the findings to build an effective vulnerability prediction model. Method: We designed and conducted experiments on the security vulnerabilities reported for Apache Tomcat (Releases 6, 7 and 8), Apache CXF and three stand-alone Java web applications of Stanford Securibench. We used machine learning and statistical techniques for predicting vulnerabilities of the systems using traceable patterns and metrics as features. Result: We found that patterns have a lower false negative rate and higher recall in detecting vulnerable code than the traditional software metrics. We also found a set of patterns and metrics that shows higher recall in vulnerability prediction. Conclusion: Based on the results of the experiments, we proposed a prediction model using patterns and metrics to better predict vulnerable code with higher recall rate. We evaluated the model for the systems under study. We also evaluated their performance in the cross-dataset validation.