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

Towards Benchmarking User Stories Estimation with COSMIC Function Points-A Case Example of Participant Observatio

Shorter time-to-market and unstable requirements is leading to introduction of Agile and DevOps practices. Story point estimation is becoming handier in Agile/DevOps setting. However, developing user stories and defining sizing unit in terms of story point is subjective process. It lacks benchmarking/standardization in terms of sizing measure and productivity of historical data. COSMIC has been considered as FSM (Functional Size Method). It has flexibility to receive requirements as User stories (popular Agile/Devops method) and derive COSMIC functional units using parametric approach. COSMIC method reduces the subjectivity and populates the productivity parameter for benchmarking. It standardizes the estimation process and can be easily deployed in Agile or DevOps setting. This paper presents the related work on linkages between User stories, COSMIC methods and traditional function point methods. It also presents the outcome of the industry survey conducted on 49 practitioners working in 10 different domains with respective to parametric estimation process adoption and presents 9 real-time case studies developed to demonstrate the usage of COSMIC method in various domains. This paper also attempts to derive mapping process of COSMIC functional process with User Stories with the help of 2 real-time industry case studies

Using locally weighted regression to estimate the functional size of software: a preliminary study

In software engineering, measuring software functional size via the IFPUG (International Function Point Users Group) Function Point Analysis using the standard manual process can be a long and expensive activity. To solve this problem, several early estimation methods have been proposed and have become de facto standard processes. Among these, a prominent one is High-level Function Point Analysis. Recently, the Simple Function Point method has been released by IFPUG; although it is a proper measurement method, it has a great level of convertibility to traditional Function Points and may be used as an estimation method. Both High-level Function Point Analysis and Simple Function Point skip the difficult and time-consuming activities needed to weight data and transaction functions. This makes the process faster and cheaper, but yields approximate measures. The accuracy of the mentioned method has been evaluated, also via large-scale empirical studies, showing that the yielded approximate measures are sufficiently accurate for practical usage. In this paper, locally weighted regression is applied to the problem outlined above. This empirical study shows that estimates obtained via locally weighted regression are more accurate than those obtained via High-level Function Point Analysis, but are not substantially better than those yielded by alternative estimation methods using linear regression. The Simple Function Point method appears to yield measures that are well correlated with those obtained via standard measurement. In conclusion, locally weighted regression appears to be effective and accurate enough for estimating software functional size

Categorical variable segmentation model for software development effort estimation

This paper proposes a new software development effort estimation model. The new model's design is based on the function point analysis, categorical variable segmentation (CVS), and stepwise regression. The stepwise regression method is used for the creation of the unique estimation model of each segment. The estimation accuracy of the proposed model is compared to clustering-based models and the international function point user group model. It is shown that the proposed model increases estimation accuracy when compared to baseline methods: non-clustered functional point analysis and clustering-based models. The new CVS model achieves a significantly higher accuracy than the baseline methods. © 2013 IEEE.Faculty of Applied Informatics, Tomas Bata University in Zlin [RO30186021025/2102

Using Locally Weighted Regression to Estimate the Functional Size of Software: an Empirical Study

In software engineering, measuring software functional size via the IFPUG (International Function Point Users Group) Function Point Analysis using the standard manual process can be a long and expensive activity, which is possible only when functional user requirements are known completely and in detail. To solve this problem, several early estimation methods have been proposed and have become de facto standard processes. Among these, a prominent one is High-level Function Point Analysis. Recently, the Simple Function Point method has been released by IFPUG; although it is a proper measurement method, it has a great level of convertibility to traditional Function Points and may be used as an estimation method. Both High-level Function Point Analysis and Simple Function Point skip the activities needed to weight data and transaction functions, thus enabling lightweight measurement based on coarse-grained requirements specifications. This makes the process faster and cheaper, but yields approximate measures. The accuracy of the mentioned method has been evaluated, also via large-scale empirical studies, showing that the yielded approximate measures are sufficiently accurate for practical usage. In this paper, locally weighted regression is applied to the problem outlined above. This empirical study shows that estimates obtained via locally weighted regression are more accurate than those obtained via High-level Function Point Analysis, but are not substantially better than those yielded by alternative estimation methods using linear regression. The Simple Function Point method appears to yield measures that are well correlated with those obtained via standard measurement. In conclusion, locally weighted regression appears to be effective and accurate enough for estimating software functional size

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

Contracting Agile Developments for Mission Critical Systems in the Public Sector

Although Agile is a well established software development paradigm, major concerns arise when it comes to contracting issues between a software consumer and a software producer. How to contractualize the Agile production of software, especially for security & mission critical organizations, which typically outsource software projects, has been a major concern since the beginning of the \u201cAgile Era.\u201d In literature, little has been done, from a foundational point of view regarding the formalization of such contracts. Indeed, when the development is outsourced, the management of the contractual life is non\u2013trivial. This happens because the interests of the two parties are typically not aligned. In these situations, software houses strive for the minimization of the effort, while the customer commonly expects high quality artifacts. This structural asymmetry can hardly be overcome with traditional \u201cWaterfall\u201d contracts. In this work, we propose a foundational approach to the Law & Economics of Agile contracts. Moreover, we explore the key elements of the Italian procurement law and outline a suitable solution to merge some basic legal constraints with Agile requirements. Finally, a case study is presented, describing how Agile contracting has been concretely implemented in the Italian Defense Acquisition Process. This work is intended to be a framework for Agile contracts for the Italian public sector of critical systems, according to the new contractual law (Codice degli Appalti)