A Systematic Mapping of Factors Affecting Accuracy of Software Development Effort Estimation

Software projects often do not meet their scheduling and budgeting targets. Inaccurate estimates are often responsible for this mismatch. This study investigates extant research on factors that affect accuracy of software development effort estimation. The purpose is to synthesize existing knowledge, propose directions for future research, and improve estimation accuracy in practice. A systematic mapping study (a comprehensive review of existing research) is conducted to identify such factors and their impact on estimation accuracy. Thirty-two factors assigned to four categories (estimation process, estimator’s characteristics, project to be estimated, and external context) are identified in a variety of research studies. Although the significant impact of several factors has been shown, results are limited by the lack of insight into the extent of these impacts. Our results imply a shift in research focus and design to gather more in-depth insights. Moreover, our results emphasize the need to argue for specific design decisions to enable a better understanding of possible influences of the study design on the credibility of the results. For software developers, our results provide a useful map to check the assumptions that undergird their estimates, to build comprehensive experience databases, and to adequately staff design projects

Fuzzy Cognitive Map based Prediction Tool for Schedule Overrun

The main aim of any software development organizations is to finish the project within acceptable or customary schedule and budget Software schedule overrun is one of a question that needs more concentration Schedule overrun may affect the whole project success like cost quality and increases risks Schedule overrun can be reason of project failure In today s competitive world controlling the schedule slippage of software project development is a challenging task Effective handling of schedule is an essential need for any software project organization The main tasks for software development estimation are determining the effort cost and schedule of developing the project under consideration Underestimation of project done knowingly just to win contract results into loses and also the poor quality project So precise schedule prediction leads to efficient control of time and budget during software development In this paper we developed a new technique for the prediction of schedule overrun This paper also presents the comparison with other algorithms of schedule estimation and Tool developed by us and at last proved that Fuzzy cognitive map based prediction tool gives more accurate results than other training algorithm

The usage of ISBSG data fields in software effort estimation: A systematic mapping study

[EN] The International Software Benchmarking Standards Group (ISBSG) maintains a repository of data about completed software projects. A common use of the ISBSG dataset is to investigate models to estimate a software project's size, effort, duration, and cost. The aim of this paper is to determine which and to what extent variables in the ISBSG dataset have been used in software engineering to build effort estimation models. For that purpose a systematic mapping study was applied to 107 research papers, obtained after a filtering process, that were published from 2000 until the end of 2013, and which listed the independent variables used in the effort estimation models. The usage of ISBSG variables for filtering, as dependent variables, and as independent variables is described. The 20 variables (out of 71) mostly used as independent variables for effort estimation are identified and analysed in detail, with reference to the papers and types of estimation methods that used them. We propose guidelines that can help researchers make informed decisions about which ISBSG variables to select for their effort estimation models.González-Ladrón-De-Guevara, F.; Fernández-Diego, M.; Lokan, C. (2016). The usage of ISBSG data fields in software effort estimation: A systematic mapping study. Journal of Systems and Software. 113:188-215. doi:10.1016/j.jss.2015.11.040S18821511

Factors Influencing the Effort of EAI Projects – A Repertory Grid Investigation

Today’s enterprises often face heterogeneous application landscapes. Many of those companies struggle with effective and efficient accomplishment of enterprise application integration (EAI), which results in significant time and budget overruns. As regards EAI project management, a major reason for failure is considered to be underestimation of effort. The underestimation has been found to be an aftermath of applying estimation methods that do not account for all relevant factors influencing EAI project effort. We therefore explore factors affecting the effort of such projects in this study. Applying Repertory Grid, we conduct 22 semi-structured expert interviews. 91 factors influencing the effort of EAI projects in nine categories emerge from these interviews. We provide an extensive overview of effort-influencing factors and their classification, which can be used as a checklist in EAI projects. Future research can additionally use our findings as basis for development of more accurate effort estimation models

Effort Estimation in Agile Software Development: A Systematic Map Study

Introduction − Making effort estimation as accurate and suitable for software development projects becomes a fundamental stage to favor its success, which is a difficult task, since the application of these techniques in constant changing agile development projects raises the need to evaluate different methods frequently.  Objectives− The objective of this study is to provide a state of the art on techniques of effort estimation in agile software development (ASD), performance evaluation and the drawbacks that arise in its application.  Method− A systematic mapping was developed involving the creation of research questions to provide a layout of this study, analysis of related words for the implementation of a search query to obtain related studies, application of exclusion, inclusion, and quality criteria to filter nonrelated studies and finally the organization and extraction of the necessary information from each study.   Results− 25 studies were selected; the main findings are: the most applied estimation techniques in agile contexts are: Estimation of Story Points (SP) followed by Planning Poker (PP) and Expert Judgment (EJ). The most frequent solutions supported in computational techniques such as: Naive Bayes, Regression Algorithms and Hybrid System; also, the performance evaluation measures Mean Magnitude of Relative Error (MMRE), Prediction Assessment (PRED) and Mean Absolute Error (MAE) have been found to be the most commonly used. Additionally, parameters such as feasibility, experience, and the delivery of expert knowledge, as well as the constant particularity and lack of data in the process of creating models to be applied to a limited number of environments are the challenges that arise the most when estimating software in agile software development (ASD)    Conclusions− It has been found there is an increase in the number of articles that address effort estimation in agile development, however, it becomes evident the need to improve the accuracy of the estimation by using estimation  techniques supported in machine learning  that have been shown to facilitate and improve the performance of this.  Key Words − Effort Estimation; Agile Software Development; Issues and Challenges; Automatic Learning; Performance Metrics  Introducción − Realizar una estimación de esfuerzo lo más precisa y adecuada para proyectos de desarrollo de software, se ha convertido en pieza fundamental para favorecer el éxito y desarrollo de los mismos, sin embargo, aplicar este tipo de estimación en proyectos de desarrollo ágil, en donde los cambios son constantes, la convierte en una tarea muy compleja de implementar.    Objetivo− El objetivo de este estudio es proveer un estado del arte sobre técnicas de estimación de esfuerzo en desarrollo de software ágil, la evaluación de su desempeño y los inconvenientes que se presentan en su aplicación.    Metodología− Se desarrolló un mapeo sistemático que involucró la creación de preguntas de investigación con el fin de proveer una estructura a seguir, análisis de palabras relacionadas con el tema de investigación para la creación e implementación de una cadena de búsqueda para la identificación de estudios relacionados con el tema, aplicación de criterios de exclusión, inclusión y calidad a los artículos encontrados para poder descartar estudios no relevantes y finalmente la organización y extracción de la información necesaria de cada artículo.     Resultados− De los 25 estudios seleccionados; los principales hallazgos son: las técnicas de estimación más aplicadas en contextos ágiles son: Estimación por medio de Puntos de Historia (SP) seguidos de Planning Poker (PP) y Juicio de Expertos (EJ). Soluciones soportadas en técnicas computacionales como: Naive Bayes, Algoritmos de Regresión y Sistema Híbridos; también se ha encontrado que la Magnitud Media del Error Relativo (MMRE), la Evaluación de la Predicción (PRED) y Error Absoluto Medio (MAE) son las medidas de evaluación de desempeño más usadas. Adicionalmente, se ha encontrado que parámetros como la viabilidad, la experiencia y la entrega de conocimiento de expertos, así como la constante particularidad y falta de datos en el proceso de creación de modelos para aplicarse a un limitado número de entornos son los desafíos que más se presentan al momento de realizar estimación de software en el desarrollo de software ágil (ASD)    Conclusiones− Se ha encontrado que existe un aumento en la cantidad de artículos que abordan la estimación de esfuerzo en el desarrollo ágil, sin embargo, se hace evidente la necesidad de mejorar la precisión de la estimación mediante el uso de técnicas de estimación soportadas en el aprendizaje de máquina que han demostrado que facilita y mejora el desempeño de este.    

Healthy or Not: A Way to Predict Ecosystem Health in GitHub

With the development of open source community, through the interaction of developers, the collaborative development of software, and the sharing of software tools, the formation of open source software ecosystem has matured. Natural ecosystems provide ecological services on which human beings depend. Maintaining a healthy natural ecosystem is a necessity for the sustainable development of mankind. Similarly, maintaining a healthy ecosystem of open source software is also a prerequisite for the sustainable development of open source communities, such as GitHub. This paper takes GitHub as an example to analyze the health condition of open source ecosystem and, also, it is a research area in Symmetry. Firstly, the paper presents the healthy definition of GitHub open source ecosystem health and, then, according to the main components of natural ecosystem health, the paper proposes the health indicators and health indicators evaluation method. Based on the above, the GitHub ecosystem health prediction method is proposed. By analyzing the projects and data collected in GitHub, it is found that, using the proposed evaluation indicators and method, we can analyze the healthy development trend of the GitHub ecosystem and contribute to the stability of ecosystem development

“Best Practice” without Evidence – Agile Software Methodology as Example

Despite the essentiality of education, and the widely known unscientific nature of expert opinion, education in general appears to be based on expert opinion. The example analyzed herein is of Agile software methodology, which is deemed a best practice and therefore taught in most IT studies, in Norway and most probably internationally. This is despite that it appears to be a well known fact within its respective field that the Agile methodology lacks scientific justification. A tertiary analysis was conducted to test this well known fact and to serve as basis for exploring what should be considered sufficient evidence for inclusion within official education. The result of the tertiary study is that, indeed, the evidence for the Agile methodology is scarce at best. A method to avoid such mistake is suggested, which could be valuable to science in general. This method entails employing philosophers of science, epistemologists, to counteract potential expert biases and verify the curriculum before it is accepted in official education

Integrating IVHM and Asset Design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collection of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process