Revisión sistemática de estudios realizados sobre comparaciones de los métodos de estimación de tamaño funcional IFPUG FPA y COSMIC sobre proyectos SOA

En la Ingeniería de Software, la estimación de proyectos es considerado un tema importante pues ayuda a la mejora del desarrollo del proyecto. Dentro de las diversas variables a estimar, tres son las más relevantes: el tamaño del software, el esfuerzo y el cronograma. Como la estimación de costos radica básicamente en estimar el tamaño de software así como la cantidad de personas necesarias para desarrollar el producto, se ha decidido centrar el estudio en la estimación del tamaño del software. Ahora, el tamaño de software puede ser cuantificado usando diferentes técnicas, como las líneas de código y los métodos de medición de tamaño funcional, etc. Nosotros nos centraremos en analizar los métodos IFPUG FPA y COSMIC. Por esta razón, la presente tesis presentará una revisión sistemática de estudios realizados sobre comparaciones de los métodos de estimación de tamaño funcional IFPUG FPA y COSMIC sobre proyectos SOA. El objetivo será el poder encontrar y analizar los diferentes trabajos que se han realizado para adaptar los métodos de estimación de tamaño funcional IFPUG FPA y COSMIC sobre proyectos SOA. Para lograr ello, se ha desarrollado esta tesis en seis capítulos. En el primero, se plantean las definiciones de los métodos de estimación IFPUG FPA y COSMIC, y el concepto SOA. En el segundo, se incluye la definición de una revisión sistemática así como los trabajos realizados de revisiones sistemáticas aplicadas a proyectos SOA. En el tercero, se presenta la planificación de la aplicación de la revisión sistemática donde se incluyen el desarrollo del protocolo, la formulación de las preguntas de investigación y la estrategia para la búsqueda. En el cuarto, se presenta la aplicación de la revisión sistemática. En el quinto, se presentan los resultados de la revisión, y en el último capítulo se incluyen las conclusiones y los trabajos futuros.Tesi

Towards making functional size measurement easily usable in practice

Functional Size Measurement methods \u2013like the IFPUG Function Point Analysis and COSMIC methods\u2013 are widely used to quantify the size of applications. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified measurement methods have been proposed. This research explores easily usable functional size measurement method, aiming to improve efficiency, reduce difficulty and cost, and make functional size measurement widely adopted in practice. The first stage of the research involved the study of functional size measurement methods (in particular Function Point Analysis and COSMIC), simplified methods, and measurement based on measurement-oriented models. Then, we modeled a set of applications in a measurement-oriented way, and obtained UML models suitable for functional size measurement. From these UML models we derived both functional size measures and object-oriented measures. Using these measures it was possible to: 1) Evaluate existing simplified functional size measurement methods and derive our own simplified model. 2) Explore whether simplified method can be used in various stages of modeling and evaluate their accuracy. 3) Analyze the relationship between functional size measures and object oriented measures. In addition, the conversion between FPA and COSMIC was studied as an alternative simplified functional size measurement process. Our research revealed that: 1) In general it is possible to size software via simplified measurement processes with acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. The models obtained from out dataset yielded results that are similar to those reported in the literature. All simplified measurement methods that use predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to functional size tend to be quite inaccurate. In general, different methods showed different accuracy for Real-Time and non Real-Time applications. 2) It is possible to write progressively more detailed and complete UML models of user requirements that provide the data required by the simplified COSMIC methods. These models yield progressively more accurate measures of the modeled software. Initial measures are based on simple models and are obtained quickly and with little effort. As V models grow in completeness and detail, the measures increase their accuracy. Developers that use UML for requirements modeling can obtain early estimates of the applications\u2018 sizes at the beginning of the development process, when only very simple UML models have been built for the applications, and can obtain increasingly more accurate size estimates while the knowledge of the products increases and UML models are refined accordingly. 3) Both Function Point Analysis and COSMIC functional size measures appear correlated to object-oriented measures. In particular, associations with basic object- oriented measures were found: Function Points appear associated with the number of classes, the number of attributes and the number of methods; CFP appear associated with the number of attributes. This result suggests that even a very basic UML model, like a class diagram, can support size measures that appear equivalent to functional size measures (which are much harder to obtain). Actually, object-oriented measures can be obtained automatically from models, thus dramatically decreasing the measurement effort, in comparison with functional size measurement. In addition, we proposed conversion method between Function Points and COSMIC based on analytical criteria. Our research has expanded the knowledge on how to simplify the methods for measuring the functional size of the software, i.e., the measure of functional user requirements. Basides providing information immediately usable by developers, the researchalso presents examples of analysis that can be replicated by other researchers, to increase the reliability and generality of the results

Towards making functional size measurement easily usable in practice

Functional Size Measurement methods –like the IFPUG Function Point Analysis and COSMIC methods– are widely used to quantify the size of applications. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified measurement methods have been proposed. This research explores easily usable functional size measurement method, aiming to improve efficiency, reduce difficulty and cost, and make functional size measurement widely adopted in practice. The first stage of the research involved the study of functional size measurement methods (in particular Function Point Analysis and COSMIC), simplified methods, and measurement based on measurement-oriented models. Then, we modeled a set of applications in a measurement-oriented way, and obtained UML models suitable for functional size measurement. From these UML models we derived both functional size measures and object-oriented measures. Using these measures it was possible to: 1) Evaluate existing simplified functional size measurement methods and derive our own simplified model. 2) Explore whether simplified method can be used in various stages of modeling and evaluate their accuracy. 3) Analyze the relationship between functional size measures and object oriented measures. In addition, the conversion between FPA and COSMIC was studied as an alternative simplified functional size measurement process. Our research revealed that: 1) In general it is possible to size software via simplified measurement processes with acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. The models obtained from out dataset yielded results that are similar to those reported in the literature. All simplified measurement methods that use predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to functional size tend to be quite inaccurate. In general, different methods showed different accuracy for Real-Time and non Real-Time applications. 2) It is possible to write progressively more detailed and complete UML models of user requirements that provide the data required by the simplified COSMIC methods. These models yield progressively more accurate measures of the modeled software. Initial measures are based on simple models and are obtained quickly and with little effort. As V models grow in completeness and detail, the measures increase their accuracy. Developers that use UML for requirements modeling can obtain early estimates of the applications‘ sizes at the beginning of the development process, when only very simple UML models have been built for the applications, and can obtain increasingly more accurate size estimates while the knowledge of the products increases and UML models are refined accordingly. 3) Both Function Point Analysis and COSMIC functional size measures appear correlated to object-oriented measures. In particular, associations with basic object- oriented measures were found: Function Points appear associated with the number of classes, the number of attributes and the number of methods; CFP appear associated with the number of attributes. This result suggests that even a very basic UML model, like a class diagram, can support size measures that appear equivalent to functional size measures (which are much harder to obtain). Actually, object-oriented measures can be obtained automatically from models, thus dramatically decreasing the measurement effort, in comparison with functional size measurement. In addition, we proposed conversion method between Function Points and COSMIC based on analytical criteria. Our research has expanded the knowledge on how to simplify the methods for measuring the functional size of the software, i.e., the measure of functional user requirements. Basides providing information immediately usable by developers, the researchalso presents examples of analysis that can be replicated by other researchers, to increase the reliability and generality of the results

Improve software defect estimation with six sigma defect measures : empirical studies imputation techniques on ISBSG data repository with a high ratio of missing data

This research analysis work reports on a set of empirical studies tackling the research issues of improving software defect estimation models with Sigma defect measures (e.g., Sigma levels) using the ISBSG data repository with a high ratio of missing data. Three imputation techniques that were selected for this research work: single imputation, regression imputation, and stochastic regression imputation. These imputation techniques were used to impute the missing data within the variable ‘Total Number of Defects’, and were first compared with each other using common verification criteria. A further verification strategy was developed to compare and assess the performance of the selected imputation techniques through verifying the predictive accuracy of the obtained software defect estimation models form the imputed datasets. A Sigma-based classification was carried out on the imputed dataset of the better performance imputation technique on software defect estimation. This classification was used to determine at which levels of Sigma; the software projects can be best used to build software defect estimation models: which has resulted in Sigma-based datasets with Sigma ranging (e.g., dataset of software projects with a range from 3 Sigma to 4 Sigma). Finally, software defect estimation models were built on the Sigma-based datasets

Development of a scaling factors framework to improve the approximation of software functional size with COSMIC - ISO 19761

De nombreuses organisations de développement de logiciels s’efforcent de fournir des produits de haute qualité tout en gardant un équilibre entre la satisfaction du client, le calendrier et le budget. L'estimation de l'effort de développement des projets logiciel est l'un des défis majeurs de ces organisations de développement et ce défi est généralement rencontré dès les premières phases du cycle de vie du développement. Pour relever ce défi, les organisations de développement de logiciels utilisent des techniques d'estimation précoce pour obtenir des estimations de l'effort au début (c.-à-d. estimations a priori) afin d'aider les gestionnaires de projet et les responsables techniques dans la planification et la gestion des projets. L'une des approches pour l’estimation de l'effort a priori est basée sur l'approximation des fonctions attendues du logiciel. Ceci nécessite l'utilisation d'une méthode de mesure pour quantifier ces fonctions: la littérature réfère à la mesure de la taille fonctionnelle des produits logiciels - incluant les applications d'entreprise. Différentes normes internationales ont été adoptées pour mesurer la taille fonctionnelle des logiciels, telle que ISO 19761: COSMIC. Cependant, durant les premières phases du cycle de vie du développement logiciel, et plus spécifiquement dans le processus d’estimation de la taille fonctionnelle du logiciel, l'absence de spécifications complètes et détaillées des exigences logicielles est commune, ce qui entraîne de nombreux défis. Par exemple: le niveau de granularité (c.-à-d. le niveau de détail) de la spécification des exigences fonctionnelles du logiciel est identifié subjectivement en utilisant l'intuition, l'expérience et/ou les opinions des experts du domaine; les facteurs d'échelle ne sont pas attribués; il n’y a pas une notation standardisée pour définir un ensemble standard de facteurs d'échelle que les ingénieurs des exigences peuvent affecter aux spécifications des exigences fonctionnelles des nouveaux projets de développement de logiciels afin d'identifier leur niveau de granularité. Ces défis affectent l’estimation de la taille fonctionnelle de nouveaux projets de développement de logiciels puisque le résultat de l’estimation de la taille fonctionnelle est l'une des entrées principales du processus d'estimation d'effort. Ces défis empêchent les gestionnaires des projets logiciels de construire des modèles réalistes d'estimation de l'effort pour les nouveaux projets de développement de logiciels. La motivation de ce projet de recherche est d'aider les organisations du développement logiciels et, en particulier, les gestionnaires des projets et les responsables techniques pour construire des modèles d'estimation de l’effort plus précis et ce en améliorant l'une des entrées du processus d'estimation de l'effort, afin d'améliorer la planification, la gestion et le développement des logiciels à des phases précoces du cycle de vie du développement des logiciels. Le but de ce projet de recherche est d'améliorer l'une des entrées du processus d'estimation de l'effort et en particulier la qualité de l’approximation de la taille fonctionnelle des nouveaux projets du développement des logiciels. L'objectif principal de la recherche est de concevoir un cadre de référence à être utilisé par les ingénieurs des exigences pour attribuer des facteurs d'échelle pour les premières versions de la spécification des exigences fonctionnelles du logiciel afin d’identifier leur niveau de granularité, ce qui se déroule généralement après l'étape de l'étude de faisabilité pour les nouveaux projets du développement logiciels. Pour atteindre cet objectif de recherche, les principales phases de la méthodologie de recherche sont: • la phase de recherche exploratoire: pour d'étudier l'impact du problème de recherche sur l'approximation de la taille fonctionnelle; • la phase de conception du cadre de référence: pour concevoir la cadre de référence qui attribue les facteurs d'échelle à des spécifications fonctionnelles des exigences fonctionnelles pour identifier leurs niveaux de granularité; et • la phase de vérification du cadre de référence: c’est la phase qui vérifie la convivialité du cadre de référence grâce aux différents groupes de participants ayant des profils d'expérience différents, et qui vérifie l'applicabilité de cadre de référence avec une variété d'études de cas représentant des systèmes logiciels différents. Le principal résultat de ce projet de recherche est un cadre de référence qui se compose: • d'un méta-modèle qui identifie les concepts et leurs relations qui doivent être recueillies par les ingénieurs des exigences pour atteindre la pleine spécification fonctionnelle des spécifications des exigences logicielles; et • les critères qui permettent d'identifier le niveau de granularité de la spécification des exigences logicielles, et de leur attribuer des facteurs d'échelle pour classer leurs niveaux de granularité. Le cadre de référence a été vérifié pour utilisation avec la même étude de cas par trois groupes de participants de l'industrie du génie logiciel, tandis que son applicabilité a été vérifiée avec quatre études de cas