Multicriteria Decision Making (MCDM) Methods for Ranking Estimation Techniques in Extreme Programming

It is essential to use multicriteria decision making (MCDM) methods to evaluate human judgments, for decision problems requiring the measuring of tangible and intangible criteria. Among the MCDM techniques, the analytic hierarchical process (AHP) and its extended version, the analytic network process (ANP) are the most powerful methodologies for ranking options and alternatives. They have been utilized by many scientists and researchers in numerous fields, especially for complex engineering problems. Both tools allow leaders to structure their issues numerically utilizing individual judgments. In this article, it is suggested that the MCDM can be useful in agile processes where complicated decisions happen routinely. This paper shows the ranking of the extreme programming (XP) estimation methods using AHP and ANP in educational and industrial environments

Metodología para la estimación de proyectos de desarrollo de software para la empresa Sophos Banking Solutions S.A.S.

Sophos Banking Solutions S.A.S. es una empresa colombiana que hace parte del portafolio de la BVC, cuenta con sedes en Bogotá, Medellín, Ciudad de México y Santiago de Chile, fue creada con capital hindú y se dedica al desarrollo de software (productos estandarizados o a la medida), la consultoría y los servicios de pruebas de software, con orientación al sector financiero -- Como cualquier negocio dedicado a estas actividades, tiene la necesidad de estimar acertadamente el esfuerzo requerido para desarrollar los proyectos solicitados por sus clientes -- Esta actividad es de alta relevancia en este tipo de negocios ya que de ella se desprende la asignación de recursos, el tiempo que cada uno de ellos dedicará y por ende, los costos totales del proyecto, en función de los cuales se establece el precio final del proyecto -- Cualquier error en estas fases de estimación afecta financieramente a la compañía, ya que podría subestimar el tiempo de entrega y el precio en la oferta al cliente -- Con esto en mente, fue necesario desarrollar una metodología confiable que incremente la asertividad de las estimaciones -- Para ello, se realizó una exploración bibliográfica del estado del arte en el tema y a partir de allí se propuso una metodología simple para que Sophos, o empresas similares, puedan estimar de una manera más confiable sus proyecto

An algorithmic-based software change effort prediction model using change impact analysis for software development

Software changes are inevitable due to the dynamic nature of the software development project itself. Some software development projects practice their own customised methodology but mostly adopt two kinds of methodologies; Traditional and Agile. Traditional methodology emphasizes on detailed planning, comprehensive documentation and extensive design that resulted a low rate of changes acceptance. In contrast, Agile methodology gives high priority on accepting changes at any point of time throughout the development process as compared to the Traditional methodology. Among the primary factor that has direct impact on the effectiveness of the change acceptance decision is the accuracy of the change effort prediction. There are two current models that have been widely used to estimate change effort which are algorithmic and non-algorithmic models. The algorithmic model is known for its formal and structural way of estimation and best suited for Traditional methodology. While non-algorithmic model is widely adopted for Agile methodology of software projects due to its easiness and requiring less work in term of effort predictability. The main issue is that none of the existing change effort prediction models is proven to suits for both, Traditional and Agile methodology. Additionally, there is as yet no clear evidence of the most accurate change effort prediction model for software development phase. One of the method to overcome these challenges is the inclusion of change impact analysis in the estimation process. The aim of the research is to overcome the challenges of change effort prediction for software development phase: inconsistent states of software artifacts, repeatability using algorithmic approach and applicability for both Traditional and Agile methodologies. This research proposed an algorithmic change effort prediction model that used change impact analysis method to improve the accuracy of the effort estimation. The proposed model used a current selected change impact analysis method for software development phase which is the SDP-CIAF (Software Development Phase-Change Impact Analysis Framework). A software prototype was also developed to support the implementation of the model. The proposed model was evaluated through an extensive experimental validation using case scenarios of six real Traditional and Agile methodologies software projects. A comparative study was also conducted for further validation and verification of the proposed model. The analysis result showed an accuracy improvement of 13.44% average mean difference for change effort prediction over the current selected change effort prediction model. The evaluation results also confirmed the applicability for both Traditional and Agile methodologies

A new approach to calibrating functional complexity weight in software development effort estimation

Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.RVO/FAI/2021/002Faculty of Applied Informatics, Tomas Bata University in Zlin [RVO/FAI/2021/002