The Experts below are selected from a list of 183 Experts worldwide ranked by ideXlab platform

Andre Augusto Tissot - One of the best experts on this subject based on the ideXlab platform.

  • ferramenta de apoio ao ensino de estimativa de software com Planning Poker
    Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), 2015
    Co-Authors: Andre Augusto Tissot, Maria Claudia Figueiredo Pereira Emer, Laudelino Cordeiro Bastos
    Abstract:

    This study describes the development and utilization of a web application to support the teaching of Software Estimation with Planning Poker and the data analysis of the impact of revising previous similar software estimates when conducting software estimates in a Planning Poker context. The behavior of 14 teams that made estimates was analyzed. Among these teams, 11 of them had an improvement in accuracy. In only 3 of them, estimates had a decreased in accuracy. The use of the software tool improved estimation of effort in most of the cases.

  • influence of the review of executed activities utilizing Planning Poker
    Brazilian Symposium on Software Engineering, 2015
    Co-Authors: Andre Augusto Tissot, Maria Claudia Figueiredo Pereira Emer, Laudelino Cordeiro Bastos
    Abstract:

    Background -- The software effort estimation research area aims to improve the accuracy of this estimation in software projects and activities. Aims -- This study describes the development and usage of a web application to collect the generated data from the Planning Poker estimation process and the analysis of the collected data to investigate the impact of revising previous estimates when conducting similar new estimates in a Planning Poker context. Method -- Software activities were estimated by UTFPR students, using Planning Poker, with and without revising previous similar activities, storing data regarding the decision-making process. And the collected data was used to investigate the impact that revising similar executed activities have in the software effort estimates' accuracy. Obtained Results -- The UTFPR students were divided into 14 groups. Eight of them showed accuracy increase in more than half of their estimates. Three of them had almost the same accuracy in more than half of their estimates. And only three of them had accuracy decrease in more than half of their estimates. Conclusion -- Reviewing the similar executed software activities, when using Planning Poker, led to more accurate software estimates in most cases, and, because of that, can improve the software development process.

  • influencia da revisao de atividades executadas para melhoria da acuracia na estimativa de software utilizando Planning Poker
    2015
    Co-Authors: Andre Augusto Tissot
    Abstract:

    Introducao – A area de pesquisa de estimativa de esforco de software busca melhorar a acuracia das estimativas de projetos e atividades de software. Objetivo – Este trabalho descreve o desenvolvimento e uso de uma ferramenta web de coleta de dados gerados durante a execucao da tecnica de estimativa Planning Poker e a analise dos dados coletados para investigacao do impacto da revisao de dados historicos de esforco. Metodo – Foram realizadas estimativas com e sem revisao, em experimentos com alunos de computacao da Universidade Tecnologica Federal do Parana, coletando os dados relacionados a tomada de decisao em uma ferramenta web. Apos isso, foi analisado o impacto causado pelas revisoes na acuracia da estimativa de esforco de software utilizando Planning Poker. Resultados Obtidos – Foi analisado o comportamento de 14 grupos de estimativas. Dentre esses times, 8 deles tiveram uma melhora na acuracia maior que 50% das estimativas analisadas. Em 3 deles, a soma das estimativas que tiveram melhora com as estimativas que permaneceram estaveis ultrapassou os 50%. Em apenas 3 deles, as estimativas tiveram reducao de acuracia maior que 50%. Conclusoes – A Revisao de Atividades Executadas, utilizando Planning Poker, melhorou a estimativa de esforco na maioria dos casos analisados, podendo ser um importante metodo para aprimorar o processo de desenvolvimento de software.

  • influence of the review of executed activities utilizing Planning Poker influencia da revisao de atividades executadas utilizando Planning Poker
    2015
    Co-Authors: Andre Augusto Tissot, Maria Claudia Figueiredo, Pereira Emer, Laudelino Cordeiro Bastos
    Abstract:

    Background - The software effort estimation research area aims to improve the accuracy of this estimation in software projects and activities. Aims - This study describes the development and usage of a web application to collect the generated data from the Planning Poker estimation process and the analysis of the collected data to investigate the impact of revising previous estimates when conducting similar new estimates in a Planning Poker context. Method - Software activities were estimated by UTFPR students, using Planning Poker, with and without revising previous similar activities, storing data regarding the decision-making process. And the collected data was used to investigate the impact that revising similar executed activities have in the software effort estimates' accuracy. Obtained Results - The UTFPR students were divided into 14 groups. Eight of them showed accuracy increase in more than half of their estimates. Three of them had almost the same accuracy in more than half of their estimates. And only three of them had accuracy decrease in more than half of their estimates. Conclusion - Reviewing the similar executed software activities, when using Planning Poker, led to more accurate software estimates in most cases, and, because of that, can improve the software development process.

Laudelino Cordeiro Bastos - One of the best experts on this subject based on the ideXlab platform.

  • ferramenta de apoio ao ensino de estimativa de software com Planning Poker
    Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação - SBIE), 2015
    Co-Authors: Andre Augusto Tissot, Maria Claudia Figueiredo Pereira Emer, Laudelino Cordeiro Bastos
    Abstract:

    This study describes the development and utilization of a web application to support the teaching of Software Estimation with Planning Poker and the data analysis of the impact of revising previous similar software estimates when conducting software estimates in a Planning Poker context. The behavior of 14 teams that made estimates was analyzed. Among these teams, 11 of them had an improvement in accuracy. In only 3 of them, estimates had a decreased in accuracy. The use of the software tool improved estimation of effort in most of the cases.

  • influence of the review of executed activities utilizing Planning Poker
    Brazilian Symposium on Software Engineering, 2015
    Co-Authors: Andre Augusto Tissot, Maria Claudia Figueiredo Pereira Emer, Laudelino Cordeiro Bastos
    Abstract:

    Background -- The software effort estimation research area aims to improve the accuracy of this estimation in software projects and activities. Aims -- This study describes the development and usage of a web application to collect the generated data from the Planning Poker estimation process and the analysis of the collected data to investigate the impact of revising previous estimates when conducting similar new estimates in a Planning Poker context. Method -- Software activities were estimated by UTFPR students, using Planning Poker, with and without revising previous similar activities, storing data regarding the decision-making process. And the collected data was used to investigate the impact that revising similar executed activities have in the software effort estimates' accuracy. Obtained Results -- The UTFPR students were divided into 14 groups. Eight of them showed accuracy increase in more than half of their estimates. Three of them had almost the same accuracy in more than half of their estimates. And only three of them had accuracy decrease in more than half of their estimates. Conclusion -- Reviewing the similar executed software activities, when using Planning Poker, led to more accurate software estimates in most cases, and, because of that, can improve the software development process.

  • influence of the review of executed activities utilizing Planning Poker influencia da revisao de atividades executadas utilizando Planning Poker
    2015
    Co-Authors: Andre Augusto Tissot, Maria Claudia Figueiredo, Pereira Emer, Laudelino Cordeiro Bastos
    Abstract:

    Background - The software effort estimation research area aims to improve the accuracy of this estimation in software projects and activities. Aims - This study describes the development and usage of a web application to collect the generated data from the Planning Poker estimation process and the analysis of the collected data to investigate the impact of revising previous estimates when conducting similar new estimates in a Planning Poker context. Method - Software activities were estimated by UTFPR students, using Planning Poker, with and without revising previous similar activities, storing data regarding the decision-making process. And the collected data was used to investigate the impact that revising similar executed activities have in the software effort estimates' accuracy. Obtained Results - The UTFPR students were divided into 14 groups. Eight of them showed accuracy increase in more than half of their estimates. Three of them had almost the same accuracy in more than half of their estimates. And only three of them had accuracy decrease in more than half of their estimates. Conclusion - Reviewing the similar executed software activities, when using Planning Poker, led to more accurate software estimates in most cases, and, because of that, can improve the software development process.

N C Haugen - One of the best experts on this subject based on the ideXlab platform.

  • using Planning Poker for combining expert estimates in software projects
    Journal of Systems and Software, 2008
    Co-Authors: K Molokkenostvold, N C Haugen, Hans Christian Benestad
    Abstract:

    When producing estimates in software projects, expert opinions are frequently combined. However, it is poorly understood whether, when, and how to combine expert estimates. In order to study the effects of a combination technique called Planning Poker, the technique was introduced in a software project for half of the tasks. The tasks estimated with Planning Poker provided: (1) group consensus estimates that were less optimistic than the statistical combination (mean) of individual estimates for the same tasks, and (2) group consensus estimates that were more accurate than the statistical combination of individual estimates for the same tasks. For tasks in the same project, individual experts who estimated a set of control tasks achieved estimation accuracy similar to that achieved by estimators who estimated tasks using Planning Poker. Moreover, for both Planning Poker and the control group, measures of the median estimation bias indicated that both groups had unbiased estimates, because the typical estimated task was perfectly on target. A code analysis revealed that for tasks estimated with Planning Poker, more effort was expended due to the complexity of the changes to be made, possibly caused by the information provided in group discussions.

  • combining estimates with Planning Poker an empirical study
    Australian Software Engineering Conference, 2007
    Co-Authors: K Molokkenostvold, N C Haugen
    Abstract:

    Combination of expert opinion is frequently used to produce estimates in software projects. However, if, when and how to combine expert estimates, is poorly understood. In order to study the effects of a combination technique called Planning Poker, the technique was introduced in a software project for half of the tasks. The tasks estimated with Planning Poker provided: 1) group consensus estimates that were less optimistic than the mechanical combination of individual estimates for the same tasks, and 2) group consensus estimates that were more accurate than the mechanical combination of individual estimates for the same tasks. The set of control tasks in the same project, estimated by individual experts, achieved similar estimation accuracy as the Planning Poker tasks. However, for both Planning Poker and the control group, measures of the median estimation bias indicated that both groups had unbiased estimates, as the typical estimated task was perfectly on target.

Rajiv Ramnath - One of the best experts on this subject based on the ideXlab platform.

  • cost effective supervised learning models for software effort estimation in agile environments
    Computer Software and Applications Conference, 2016
    Co-Authors: Kayhan Moharreri, Alhad Vinayak Sapre, Jayashree Ramanathan, Rajiv Ramnath
    Abstract:

    Software development effort estimation is the process of predicting the most realistic effort required to develop or maintain software. It is important to develop estimation models and appropriate techniques to avoid losses caused by poor estimation. However, no method exists that is the most appropriate one for Agile Development where frequent iterations involve the customer causing time consuming estimation process. To address this an automated estimation methodology called "Auto-Estimate" is proposed complementing Agile's manual Planning Poker. The Auto-Estimate leverages features extracted from Agile story cards, and their actual effort time. The approach is justified by evaluating alternative machine learning algorithms for effort prediction. It is shown that selected machine learning methods perform better than Planning Poker estimates in the later stages of a project. This estimation approach is evaluated for accuracy, applicability and value, and the results are presented within a real-world setting.

  • cost effective supervised learning models for software effort estimation in agile environments
    COMPSAC Workshops, 2016
    Co-Authors: Kayhan Moharreri, Alhad Vinayak Sapre, Jayashree Ramanathan, Rajiv Ramnath
    Abstract:

    Software development effort estimation is the process of predicting the most realistic effort required to develop or maintain software. It is important to develop estimation models and appropriate techniques to avoid losses caused by poor estimation. However, no method exists that is the most appropriate one for Agile Development where frequent iterations involve the customer causing time consuming estimation process. To address this an automated estimation methodology called "Auto-Estimate" is proposed complementing Agile's manual Planning Poker. The Auto-Estimate leverages features extracted from Agile story cards, and their actual effort time. The approach is justified by evaluating alternative machine learning algorithms for effort prediction. It is shown that selected machine learning methods perform better than Planning Poker estimates in the later stages of a project. This estimation approach is evaluated for accuracy, applicability and value, and the results are presented within a real-world setting.

K Molokkenostvold - One of the best experts on this subject based on the ideXlab platform.

  • using Planning Poker for combining expert estimates in software projects
    Journal of Systems and Software, 2008
    Co-Authors: K Molokkenostvold, N C Haugen, Hans Christian Benestad
    Abstract:

    When producing estimates in software projects, expert opinions are frequently combined. However, it is poorly understood whether, when, and how to combine expert estimates. In order to study the effects of a combination technique called Planning Poker, the technique was introduced in a software project for half of the tasks. The tasks estimated with Planning Poker provided: (1) group consensus estimates that were less optimistic than the statistical combination (mean) of individual estimates for the same tasks, and (2) group consensus estimates that were more accurate than the statistical combination of individual estimates for the same tasks. For tasks in the same project, individual experts who estimated a set of control tasks achieved estimation accuracy similar to that achieved by estimators who estimated tasks using Planning Poker. Moreover, for both Planning Poker and the control group, measures of the median estimation bias indicated that both groups had unbiased estimates, because the typical estimated task was perfectly on target. A code analysis revealed that for tasks estimated with Planning Poker, more effort was expended due to the complexity of the changes to be made, possibly caused by the information provided in group discussions.

  • combining estimates with Planning Poker an empirical study
    Australian Software Engineering Conference, 2007
    Co-Authors: K Molokkenostvold, N C Haugen
    Abstract:

    Combination of expert opinion is frequently used to produce estimates in software projects. However, if, when and how to combine expert estimates, is poorly understood. In order to study the effects of a combination technique called Planning Poker, the technique was introduced in a software project for half of the tasks. The tasks estimated with Planning Poker provided: 1) group consensus estimates that were less optimistic than the mechanical combination of individual estimates for the same tasks, and 2) group consensus estimates that were more accurate than the mechanical combination of individual estimates for the same tasks. The set of control tasks in the same project, estimated by individual experts, achieved similar estimation accuracy as the Planning Poker tasks. However, for both Planning Poker and the control group, measures of the median estimation bias indicated that both groups had unbiased estimates, as the typical estimated task was perfectly on target.