Effort Estimation in Agile Software Development: An Updated Review

Dantas, Emanuel; Perkusich, Mirko; Dilorenzo, Ednaldo; Santos, Danilo F. S.; Almeida, Hyggo; Perkusich, Angelo

doi:10.1142/s0218194018400302

Cited by 21 publications

(24 citation statements)

References 11 publications

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“…Hay artículos investigativos donde se realizan revisiones de literaturas como lo hace Dantas en su análisis de 24 documentos relevantes relacionados con la estimación de esfuerzo en el desarrollo de software ágil y haciendo uso de un análisis temático. La revisión, por otro lado, permite y genera la necesidad de investigar más sobre las técnicas inteligentes en la estimación de esfuerzo ya que estos tienen beneficios para mejorar la gestión de proyectos agiles [16,17].…”

Section: Estado Del Arteunclassified

“…PLANNING POKER es la técnica de estimación más encontrada en la revisión actualizada realizada por Dantas [16]. Así mismo, en relación con el estudio de revisión de literatura más reciente realizado por Méndez [10], los resultados obtenidos en esté punto presentan un contraste mayor debido a que la revisión de literatura arroja un 6.5% con respecto al 43.17% del presente estudio.…”

Section: Rq2: Técnicas De Estimaciónunclassified

“…En la pregunta 3, relacionada con los predictores de esfuerzo utilizados, el 38.85% (122) de los encuestados seleccionaron Líneas de código como métrica adoptada, es prudente la adaptación de esta pues con ella se puede generar una estimación del tamaño del producto de software que se va a desarrollar [16]. Del mismo modo, otras métricas son mencionadas como STORY POINTS (33.76%) y puntos de caso de uso (23.57%).…”

Section: Rq3: Predictores De Esfuerzounclassified

“…Los puntos de caso de uso ocupan el tercer lugar en ambos casos. Al igual que el estudio realizo por Usman [21], Dantas realiza una revisión actualizada en donde concluye igualmente, que en 17 de los documentos revisados se hace uso de puntos de historia [16].…”

Section: Rq3: Predictores De Esfuerzounclassified

“…Al igual que en el estudio realizado y el análisis de las respuestas de los encuestados, la revisión actualizada realizada por Dantas [16] se evidencia que él mayoría de los documentos estudiados (11 artículos) se hace uso de SCRUM como metodología de desarrollo de software ágil.…”

Section: Rq4: Metodología áGilunclassified

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Estimation of effort in agile software development: Study of the current state in Bogotá

Bustamante

2020

ITECKNE

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Context: Over the years, numerous studies have been developed related to estimating effort in agile software development, which allow us to understand the advances that have been made and the need for their use in companies. Objective: To know the perspective of entrepreneurs from different organizations in Bogotá that work in the sector or are related to the development of agile software and those who have had projects related to the subject. Method: For the elaboration of the article, a bibliographic research is carried out. Results: The data obtained are from 314 respondents in the city of Bogotá, the main findings are: 1) For companies it is extremely important with (34%) the adoption of an effort estimation methodology. 2) The most used estimation technique is the use case point with (43.31%). 3) The most used effort predictor is the Code Line (38.85%) and followed by this STORY POINT with (33.76%). 4) The agile methodology adopted in companies is SCRUM with (43.95%). 5) As the most determining factor to estimate the effort, the team experience with (53.50%) is considered. 6) In the percentage of error between estimate of projected effort and real effort (26.75%) of the respondents determine that this 5% -25% is underestimated. 7) Finally, as a difficulty in your company to estimate the effort of projects through agile software development, the respondents recognize (26.11%) that the resources are the greatest. Conclusions: The estimation of effort is relatively variable, for many companies this depends on the needs of each one, on the other hand, the use of agile methodology and estimation techniques varies in its use depending on the cross between them and how they are used. complement each other.

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Section: Estado Del Arteunclassified

Section: Rq2: Técnicas De Estimaciónunclassified

Section: Rq3: Predictores De Esfuerzounclassified

Section: Rq4: Metodología áGilunclassified

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Estimation of effort in agile software development: Study of the current state in Bogotá

Bustamante

2020

ITECKNE

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Effort prediction for the software project construction phase

López-Martín

2021

J Software Evolu Process

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The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR-SMO-PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR-SMO-PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε-SVR and ʋ-SVR in the seven data sets. Thus, the SVR-SMO-PUK is useful to software managers to predict CE.

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