4,333 research outputs found

    A Review: Effort Estimation Model for Scrum Projects using Supervised Learning

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    Effort estimation practice in Agile is a critical component of the methodology to help cross-functional teams to plan and prioritize their work. Agile approaches have emerged in recent years as a more adaptable means of creating software projects because they consistently produce a workable end product that is developed progressively, preventing projects from failing entirely. Agile software development enables teams to collaborate directly with clients and swiftly adjust to changing requirements. This produces a result that is distinct, gradual, and targeted. It has been noted that the present Scrum estimate approach heavily relies on historical data from previous projects and expert opinion, while existing agile estimation methods like analogy and planning poker become unpredictable in the absence of historical data and experts. User Stories are used to estimate effort in the Agile approach, which has been adopted by 60–70% of the software businesses. This study's goal is to review a variety of strategies and techniques that will be used to gauge and forecast effort. Additionally, the supervised machine learning method most suited for predictive analysis is reviewed in this paper

    Effort Estimation in Agile Software Development: A Systematic Map Study

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    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.    

    An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review

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    [EN] Software developers require effective effort estimation models to facilitate project planning. Although Usman et al. systematically reviewed and synthesized the effort estimation models and practices for Agile Software Development (ASD) in 2014, new evidence may provide new perspectives for researchers and practitioners. This article presents a systematic literature review that updates the Usman et al. study from 2014 to 2020 by analyzing the data extracted from 73 new papers. This analysis allowed us to identify six agile methods: Scrum, Xtreme Programming and four others, in all of which expert-based estimation methods continue to play an important role. This is particularly the case of Planning Poker, which is very closely related to the most frequently used size metric (story points) and the way in which software requirements are specified in ASD. There is also a remarkable trend toward studying techniques based on the intensive use of data. In this respect, although most of the data originate from single-company datasets, there is a significant increase in the use of cross-company data. With regard to cost factors, we applied the thematic analysis method. The use of team and project factors appears to be more frequent than the consideration of more technical factors, in accordance with agile principles. Finally, although accuracy is still a challenge, we identified that improvements have been made. On the one hand, an increasing number of papers showed acceptable accuracy values, although many continued to report inadequate results. On the other, almost 29% of the papers that reported the accuracy metric used reflected aspects concerning the validation of the models and 18% reported the effect size when comparing models.This work was supported by the Spanish Ministry of Science, Innovation and Universities through the Adapt@Cloud Project under Grant TIN2017-84550-R.Fernández-Diego, M.; Méndez, ER.; González-Ladrón-De-Guevara, F.; Abrahao Gonzales, SM.; Insfran, E. (2020). An Update on Effort Estimation in Agile Software Development: A Systematic Literature Review. IEEE Access. 8:166768-166800. https://doi.org/10.1109/ACCESS.2020.3021664S166768166800

    A survey of self organisation in future cellular networks

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    This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

    Generative Artificial Intelligence for Software Engineering -- A Research Agenda

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    Generative Artificial Intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAIs ChatGPT, GitHub Copilot, and Amazon CodeWhisperer. Although many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios. We conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for Software Engineering. We identified 78 open Research Questions (RQs) in 11 areas of Software Engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology. GenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research

    Machine Learning for Fluid Mechanics

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    The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

    Big data analytics:Computational intelligence techniques and application areas

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    Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
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