Les adultes peuvent éprouver des difficultés à discriminer des phonèmes d’une langue seconde (L2) qui ne servent pas à distinguer des items lexicaux dans leur langue maternelle (L1). Le Feature Model (FM) de Brown (1998) propose que les adultes peuvent réussir à créer des nouvelles catégories de sons seulement si celles-ci peuvent être construites à partir de traits distinctifs existant dans la L1 des auditeurs. Cette hypothèse a été testée sur plusieurs contrastes consonantiques dans différentes langues; cependant, il semble que les traits qui s’appliquent sur les voyelles n’aient jamais été examinés dans cette perspective et encore moins les traits qui opèrent à la fois dans les systèmes vocalique et consonantique et qui peuvent avoir un statut distinctif ou non-distinctif. Le principal objectif de la présente étude était de tester la validité du FM concernant le contraste vocalique oral-nasal du portugais brésilien (PB). La perception naïve du contraste /i/-/ĩ/ par des locuteurs du français, de l’anglais, de l’espagnol caribéen et de l’espagnol conservateur a été examinée, étant donné que ces quatre langues diffèrent en ce qui a trait au statut de la nasalité. De plus, la perception du contraste non-naïf /e/-/ẽ/ a été inclus afin de comparer les performances dans la perception naïve et non-naïve. Les résultats obtenus pour la discrimination naïve de /i/-/ĩ/ a permis de tirer les conclusions suivantes pour la première exposition à un contraste non natif : (1) le trait [nasal] qui opère de façon distinctive dans la grammaire d’une certaine L1 peut être redéployé au sein du système vocalique, (2) le trait [nasal] qui opère de façon distinctive dans la grammaire d’une certaine L1 ne peut pas être redéployé à travers les systèmes (consonne à voyelle) et (3) le trait [nasal] qui opère de façon non-distinctive dans la grammaire d’une certaine L1 peut être ou ne pas être redéployé au statut distinctif. En dernier lieu, la discrimination non-naïve de /e/-/ẽ/ a été réussie par tous les groupes, suggérant que les trois types de redéploiement s’avèrent possibles avec plus d’expérience dans la L2. / Adults may experience difficulties discriminating phonemes of a second language (L2) that do not serve to distinguish lexical items in their native language (L1). Brown’s (1998) Feature Model (FM) advances that adults may be able to create new sound categories only if these can be built from contrastive features existing in their L1. This hypothesis has been tested on various consonant contrasts in a number of languages; however, it appears that features applying on vowels have never been examined from this perspective and neither have features that operate both in the vowel and the consonant systems and that may have a contrastive or a non-contrastive status. The main purpose of the present study was to test the validity of the FM with respect to the oral-nasal vowel contrast of Brazilian Portuguese. The naïve perception of the contrast /i/-/ĩ/ by French, English, Caribbean Spanish, and conservative Spanish speakers was examined, given that these four languages differ with respect to the status of nasality. Moreover, the perception of the non-naïve contrast /e/-/ẽ/ was included to compare naïve and non-naïve perception performances. The obtained data for the naïve discrimination of /i/-/ĩ/ allowed to draw the following conclusions for the first exposure to a non-native contrast: (1) the feature [nasal] operating contrastively in the grammar of a given L1 can be redeployed within the vowel system, (2) the feature [nasal] operating contrastively in the grammar of a given L1 may not be redeployed across systems (consonant to vowel), and (3) the feature [nasal] operating non-contrastively in the grammar of a given L1 might or might not be redeployed to contrastive status. Lastly, the non-naïve perception of /e/-/ẽ/ was successful for all groups, suggesting that the three types of redeployment are possible with more experience in the L2.

Configuração modelo de features é conhecida por ser uma atividade complexa, demorada e propensa a erros. Esta atividade torna-se ainda mais complicada quando envolve múltiplas partes interessadas no processo de configuração. Trabalhos de pesquisa têm proposto abordagens para ajudar na configuração de modelo de features, mas elas dependem de processos sistemáticos que restringem as decisões de alguns dos stakeholders. Neste trabalho, propomos uma nova abordagem para melhorar o processo de configuração multi-stakeholder, considerando as preferências dos stakeholders expressas através de restrições duras e brandas. Com base em tais preferências, recomendamos diferentes configurações de produto utilizando diferentes estratégias da teoria da escolha social. Nossa abordagem é implementada em uma ferramenta chamada SACRES, que permite criar grupos de stakeholders, especificar preferências dos stakeholders sobre uma configuração e gerar as configurações ideais. Realizamos um estudo empírico para avaliar a eficácia de nossas estratégias no que diz respeito à satisfação individual e justiça entre todos os stakeholders. Os resultados obtidos provem evidência de que estratégias em particular possuem melhor performance em relação à satisfação de grupo, chamadas average e multiplicative considerando as pontuações atribuídas pelos participantes e complexidade computacional. Nossos resultados são relevantes não só no contexto de Linha de Produto de Software, mas também para a Teoria da Escolha Social, dada a instanciação de estratégias de escolha social em um problema prático. / Feature model con guration is known to be a hard, error-prone and timeconsuming activity. This activity gets even more complicated when it involves multiple stakeholders in the con guration process. Research work has proposed approaches to aid multi-stakeholder feature model con guration, but they rely on systematic processes that constraint decisions of some of the stakeholders. In this dissertation, we propose a novel approach to improve the multi-stakeholder con guration process, considering stakeholders' preferences expressed through both hard and soft constraints. Based on such preferences, we recommend di erent product con gurations using di erent strategies from the social choice theory. Our approach is implemented in a tool named SACRES, which allows creation of stakeholder groups, speci cation of stakeholder preferences over a con guration and generation of optimal con guration. We conducted an empirical study to evaluate the e ectiveness of our strategies with respect to individual stakeholder satisfaction and fairness among all stakeholders. The obtained results provide evidence that particular strategies perform best with respect to group satisfaction, namely average and multiplicative, considering the scores given by the participants and computational complexity. Our results are relevant not only in the context software product lines, but also in the context of social choice theory, given the instantiation of social choice strategies in a practical problem.

Configuração modelo de features é conhecida por ser uma atividade complexa, demorada e propensa a erros. Esta atividade torna-se ainda mais complicada quando envolve múltiplas partes interessadas no processo de configuração. Trabalhos de pesquisa têm proposto abordagens para ajudar na configuração de modelo de features, mas elas dependem de processos sistemáticos que restringem as decisões de alguns dos stakeholders. Neste trabalho, propomos uma nova abordagem para melhorar o processo de configuração multi-stakeholder, considerando as preferências dos stakeholders expressas através de restrições duras e brandas. Com base em tais preferências, recomendamos diferentes configurações de produto utilizando diferentes estratégias da teoria da escolha social. Nossa abordagem é implementada em uma ferramenta chamada SACRES, que permite criar grupos de stakeholders, especificar preferências dos stakeholders sobre uma configuração e gerar as configurações ideais. Realizamos um estudo empírico para avaliar a eficácia de nossas estratégias no que diz respeito à satisfação individual e justiça entre todos os stakeholders. Os resultados obtidos provem evidência de que estratégias em particular possuem melhor performance em relação à satisfação de grupo, chamadas average e multiplicative considerando as pontuações atribuídas pelos participantes e complexidade computacional. Nossos resultados são relevantes não só no contexto de Linha de Produto de Software, mas também para a Teoria da Escolha Social, dada a instanciação de estratégias de escolha social em um problema prático. / Feature model con guration is known to be a hard, error-prone and timeconsuming activity. This activity gets even more complicated when it involves multiple stakeholders in the con guration process. Research work has proposed approaches to aid multi-stakeholder feature model con guration, but they rely on systematic processes that constraint decisions of some of the stakeholders. In this dissertation, we propose a novel approach to improve the multi-stakeholder con guration process, considering stakeholders' preferences expressed through both hard and soft constraints. Based on such preferences, we recommend di erent product con gurations using di erent strategies from the social choice theory. Our approach is implemented in a tool named SACRES, which allows creation of stakeholder groups, speci cation of stakeholder preferences over a con guration and generation of optimal con guration. We conducted an empirical study to evaluate the e ectiveness of our strategies with respect to individual stakeholder satisfaction and fairness among all stakeholders. The obtained results provide evidence that particular strategies perform best with respect to group satisfaction, namely average and multiplicative, considering the scores given by the participants and computational complexity. Our results are relevant not only in the context software product lines, but also in the context of social choice theory, given the instantiation of social choice strategies in a practical problem.

Configuração modelo de features é conhecida por ser uma atividade complexa, demorada e propensa a erros. Esta atividade torna-se ainda mais complicada quando envolve múltiplas partes interessadas no processo de configuração. Trabalhos de pesquisa têm proposto abordagens para ajudar na configuração de modelo de features, mas elas dependem de processos sistemáticos que restringem as decisões de alguns dos stakeholders. Neste trabalho, propomos uma nova abordagem para melhorar o processo de configuração multi-stakeholder, considerando as preferências dos stakeholders expressas através de restrições duras e brandas. Com base em tais preferências, recomendamos diferentes configurações de produto utilizando diferentes estratégias da teoria da escolha social. Nossa abordagem é implementada em uma ferramenta chamada SACRES, que permite criar grupos de stakeholders, especificar preferências dos stakeholders sobre uma configuração e gerar as configurações ideais. Realizamos um estudo empírico para avaliar a eficácia de nossas estratégias no que diz respeito à satisfação individual e justiça entre todos os stakeholders. Os resultados obtidos provem evidência de que estratégias em particular possuem melhor performance em relação à satisfação de grupo, chamadas average e multiplicative considerando as pontuações atribuídas pelos participantes e complexidade computacional. Nossos resultados são relevantes não só no contexto de Linha de Produto de Software, mas também para a Teoria da Escolha Social, dada a instanciação de estratégias de escolha social em um problema prático. / Feature model con guration is known to be a hard, error-prone and timeconsuming activity. This activity gets even more complicated when it involves multiple stakeholders in the con guration process. Research work has proposed approaches to aid multi-stakeholder feature model con guration, but they rely on systematic processes that constraint decisions of some of the stakeholders. In this dissertation, we propose a novel approach to improve the multi-stakeholder con guration process, considering stakeholders' preferences expressed through both hard and soft constraints. Based on such preferences, we recommend di erent product con gurations using di erent strategies from the social choice theory. Our approach is implemented in a tool named SACRES, which allows creation of stakeholder groups, speci cation of stakeholder preferences over a con guration and generation of optimal con guration. We conducted an empirical study to evaluate the e ectiveness of our strategies with respect to individual stakeholder satisfaction and fairness among all stakeholders. The obtained results provide evidence that particular strategies perform best with respect to group satisfaction, namely average and multiplicative, considering the scores given by the participants and computational complexity. Our results are relevant not only in the context software product lines, but also in the context of social choice theory, given the instantiation of social choice strategies in a practical problem.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-03-15T15:21:11Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE_SABRINA.pdf: 1152470 bytes, checksum: a89ffc94cb3ee813cf52ca2c043171ba (MD5) / Made available in DSpace on 2016-03-15T15:21:11Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) TESE_SABRINA.pdf: 1152470 bytes, checksum: a89ffc94cb3ee813cf52ca2c043171ba (MD5) Previous issue date: 2015-03-31 / Software Product Lines (SPLs) allow engineers to systematically build families of software products, defined by a unique combination of features—increments in functionality, improving both the efficiency of the software development process and the quality of the software developed. However, testing these kinds of systems is challenging, as it may require running each test against a combinatorial number of products. We call this problem the High Dimensionality Problem. Another obstacle to product line testing is the absence of Feature Models (FMs), making it difficult to discover the real causes for test failures. We call this problem the Lack of Feature Model Problem. The High Dimensionality Problem is associated to the large space of possible configurations that an SPL can reach. If an SPL has n boolean features, for example, there are 2n possible feature combinations. Therefore, systematically testing this kind of system may require running each test against all those combinations, in the worst case. The Lack of Feature Model Problem is related to the absence of feature models. The FM enables accurate categorization of failing tests as failures of programs or the tests themselves, not as failures due to inconsistent combinations of features. For this reason, the lack of FM presents a huge challenge to discover the true causes for test failures. Aiming to solve these problems, we propose two lightweight techniques: SPLat and SPLif. SPLat is a new approach to dynamically prune irrelevant configurations: the configurations to run for a test can be determined during test execution by monitoring accesses to configuration variables. As a result, SPLat reduces the number of configurations. Consequently, SPLat is lightweight compared to prior works that used static analysis and heavyweight dynamic execution. SPLif is a technique for testing SPLs that does not require a priori availability of feature models. Our insight is to use a profile of passing and failing test runs to quickly identify test failures that are indicative of a problem (in test or code) as opposed to a manifestation of execution against an inconsistent combination of features. Experimental results show that SPLat effectively identifies relevant configurations with a low overhead. We also apply SPLat on two large configurable systems (Groupon and GCC), and it scaled without much engineering effort. Experimental results demonstrate that SPLif is useful and effective to quickly find tests that fail on consistent configurations, regardless of how complete the FMs are. Furthermore, we evaluated SPLif on one large extensively tested configurable system, GCC, where it helped to reveal 5 new bugs, 3 of which have been fixed after our bug reports. / Software Product Lines (SPLs) allow engineers to systematically build families of software products, defined by a unique combination of features—increments in functionality, improving both the efficiency of the software development process and the quality of the software developed. However, testing these kinds of systems is challenging, as it may require running each test against a combinatorial number of products. We call this problem the High Dimensionality Problem. Another obstacle to product line testing is the absence of Feature Models (FMs), making it difficult to discover the real causes for test failures. We call this problem the Lack of Feature Model Problem. The High Dimensionality Problem is associated to the large space of possible configurations that an SPL can reach. If an SPL has n boolean features, for example, there are 2n possible feature combinations. Therefore, systematically testing this kind of system may require running each test against all those combinations, in the worst case. The Lack of Feature Model Problem is related to the absence of feature models. The FM enables accurate categorization of failing tests as failures of programs or the tests themselves, not as failures due to inconsistent combinations of features. For this reason, the lack of FM presents a huge challenge to discover the true causes for test failures. Aiming to solve these problems, we propose two lightweight techniques: SPLat and SPLif. SPLat is a new approach to dynamically prune irrelevant configurations: the configurations to run for a test can be determined during test execution by monitoring accesses to configuration variables. As a result, SPLat reduces the number of configurations. Consequently, SPLat is lightweight compared to prior works that used static analysis and heavyweight dynamic execution. SPLif is a technique for testing SPLs that does not require a priori availability of feature models. Our insight is to use a profile of passing and failing test runs to quickly identify test failures that are indicative of a problem (in test or code) as opposed to a manifestation of execution against an inconsistent combination of features. Experimental results show that SPLat effectively identifies relevant configurations with a low overhead. We also apply SPLat on two large configurable systems (Groupon and GCC), and it scaled without much engineering effort. Experimental results demonstrate that SPLif is useful and effective to quickly find tests that fail on consistent configurations, regardless of how complete the FMs are. Furthermore, we evaluated SPLif on one large extensively tested configurable system, GCC, where it helped to reveal 5 new bugs, 3 of which have been fixed after our bug reports.

Master of Science / Department of Computing and Information Sciences / John M. Hatcliff / Developing a wireless sensor network application includes a variety of tasks, such as coding of the implementation, designing the architecture and assessing availability of hardware components, that provide necessary capabilities. Before compiling an application, the developer has to configure the selection of hardware components and set up required parameters. One has to choose from among a variety of configurations regarding communication parameters, such as frequency, channel, subnet identifier, transmission power, etc.. This configuration step also includes setting up parameters for the selection of hardware components, such as a specific hardware platform, which sensor boards and programmer boards to be used or the use of optional services and more. Reasoning about a proper selection of configuration parameters is often very difficult, since there are a lot of dependencies among these parameters which may rule out some other options. The developer has to know about all these constraints in order to pick a valid configuration. Unfortunately, the existing makefile approach that comes with nesC is poorly organized and does not capture important compatibility constraints. The configuration of a particular nesC application is distributed in multiple makefiles. Therefore a developer has to look at multiple files to make sure all necessary parameter are set up correctly for compiling a specific application. Furthermore without analyzing all makefiles it is unclear what the total configurability of a nesC application is and what options and parameters are provided (e.g. is there a parameter for enabling secure communication). In addition to this, the makefile approach tends to be error-prone, since the developer has to type in variable names and values manually, that match the existing implementation. However, the existing configuration system does not capture important compatibility constraints, such as capabilities of selected hardware components. This thesis proposes the use of feature models to configure nesC-based sensor network applications. We provide a tool-supported framework to model valid configurations and a generator that translates this model into a makefile compatible with existing nesC infrastructure. The framework automatically rules out selection of incompatible features using a build-in constraint language. Since all variables are defined in the model, misspellings of variable names are reduced and their domains are clearly defined because most variables come with all its possible options. A developer just needs to choose one or more of them by enabling certain features, where the problem of cardinality is also handled by the model. We show a detailed analysis of nesC's variability domain and how to use feature models to cover the exact behavior of nesC's makefile approach. In a following chapter we simplify our feature model and include the selection of specific hardware components, its capabilities and its dependencies. The feature model and the makefile generator offer a convenient way to configure nesC applications, that is faster, easier to understand and to handle, more transparent and once implemented it gives the possibility to adopt this configuration tool to an existing development environment.

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models.

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models.

In this research, component oriented development is supported with design guidance by extending the Axiomatic Design Theory for component orientation, and utilizing domain engineering and ontology mechanisms. Guidance is offered in the form of suggesting missing components and discovering incompatibilities among the candidate elements of software development, corresponding to different phases such as requirement analysis, design, and implementation. A mature domain concept is developed suggesting the availability of reference models for customer needs, software system requirements, software design, and also a rich set of implemented components. As the system is being defined starting with the customer needs and progressing towards components, at every step the developer is presented what is available in the domain and what becomes unavailable. This guidance is based on the selections made so far, utilizing ontology based constraint checking. Feature Models are incorporated for modeling customer needs. Case studies are presented for demonstration purposes.

The approach proposed in this thesis contributes to implementing source code reuse and re-engineering techniques for agile software development. This work includes an introduction to feature models and some of the Feature Oriented Software Development (FOSD) practices to achieve a lightweight way of retrieving source code. A Feature model created during the course of following FOSD practices serves as an additional layer of documentation which represents the problem space for the developed application. This thesis proposes linking source code with such a feature model for the purpose of identifying and retrieving code. This mechanism helps with accessing the code segment corresponding to a feature with minimal effort, thus suits agile development methods. At the moment, there is a gap between feature oriented approaches and agile methods. This thesis tries to close this gap between high-level approaches for software modelling (feature modelling) and agile methods for software development.