User stories complexity estimation using Bayesian networks for inexperienced developers

López-Martínez, Janeth; Ramírez‐Noriega, Alan; Juárez‐Ramírez, Reyes; Licea, Guillermo; Jiménez, Samantha

doi:10.1007/s10586-017-0996-z

Cited by 20 publications

(13 citation statements)

References 24 publications

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“…Experience attribute in most studies is used to measure the implementation experience of the developer or programmer (Tanveer et al, 2016;Tanveer et al, 2017b;López-Mart´ınez et al, 2017a;López-Martínez et al, 2017b;López-Martínez et al, 2018;Malgonde & Chari, 2018). Other studies assess this attribute based on developer's experience in making estimation (Tanveer et al, 2016;Tanveer et al, 2017b).…”

Section: Approach Methodsmentioning

confidence: 99%

“…Some studies apply size attribute that represents the value of the user story (López-Martínez et al, 2017b;López-Martínez et al, 2018), the code metrics for each affected class (Tanveer et al, 2018), and the team size (Garg & Gupta, 2015). However, other studies do not detail the size attribute (Kang et al, 2010;Bilgaiyan et al, 2018;Malgonde & Chari, 2018;Tanveer et al, 2018).…”

Section: Approach Methodsmentioning

confidence: 99%

“…Effort can be assumed as effort per person (Popli & Chauhan, 2014a) or actual effort (Panda et al, 2015a;Panda et al, 2015b). However, the attribute also represents itself without supplement (López-Martínez et al, 2017a;López-Martínez et al, 2017b;López-Martínez et al, 2018;Malgonde & Chari, 2018).…”

Section: Approach Methodsmentioning

confidence: 99%

“…Commonly, the attribute of time refers to the time it takes to complete a project (Popli & Chauhan, 2013;López-Martínez et al, 2017a;López-Martínez et al, 2017b;López-Martínez et al, 2018). In some studies, it is explicitly explained as Person-hours (Desharnais et al, 2011) or Working Hours (Dragicevic et al, 2017).…”

Section: Approach Methodsmentioning

confidence: 99%

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A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

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Currently, Agile software development method has been commonly used in software development projects, and the success rate is higher than waterfall projects. The effort estimation in Agile is still a challenge because most existing means are developed based on the conventional method. Therefore, this study aimed to ascertain the software effort estimation method that is applied in Agile, the implementation approach, and the attributes that affect effort estimation. The results showed the top three estimation that is applied in Agile, are machine learning (37%), Expert Judgement (26%), and Algorithmic (21%). The implementation of all machine learning methods used a hybrid approach, which is a combination of machine learning and expert judgement, or a mix of two or more machine learning. Meanwhile, the implementation of effort estimation through a hybrid approach was only used in 47% of relevant articles. In addition, effort estimation in Agile involved twenty-four attributes, where Complexity, Experience, Size, and Time are the most commonly used and implemented.

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Section: Approach Methodsmentioning

confidence: 99%

Section: Approach Methodsmentioning

confidence: 99%

Section: Approach Methodsmentioning

confidence: 99%

Section: Approach Methodsmentioning

confidence: 99%

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A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

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“…Over the past years, there has been a growing interest in effort estimation researches [21], [37], [32]. In previous work [9], we identified different approaches proposed to estimate effort using historical data and expert opinions based techniques.…”

Section: Introductionmentioning

confidence: 99%

An Effort Estimation Support Tool for Agile Software Development: An Empirical Evaluation

Dantas

Costa

Vinicius³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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Accurate effort estimation is an important part of the software process. In Agile Software Development, the techniques for predicting effort are mostly based on expert judgment, but there are approaches based on Machine Learning. The theme continues to be challenging and a subject of further studies given the difficulty of finding accurate solutions to the problem. This paper proposes and evaluates a tool based on the decision tree method for effort estimation in agile projects. We evaluated our tool given its accuracy and ease of use collecting data from four projects. To evaluate the accuracy, we compared the values of Magnitude of Relative Error from the teams' estimations with the values provided by the tool. To evaluate the ease of use, we used the Technology Acceptance Mode. The initial results show that the tool can be reliably used with minimal training. In terms of accuracy, the tool achieved lower error compared to the estimates provided by the teams (mean: 19.05% vs 33.32%), and the evaluation means in TAM were higher than 4.0 in ten of the eleven variables analyzed on a Likert scale. From this work, we conclude that estimation by decision tree is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.

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Data-driven effort estimation techniques of agile user stories: a systematic literature review

Alsaadi

Saeedi

2022

Artif Intell Rev

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User stories complexity estimation using Bayesian networks for inexperienced developers

Cited by 20 publications

References 24 publications

A Review Article on Software Effort Estimation in Agile Methodology

A Review Article on Software Effort Estimation in Agile Methodology

An Effort Estimation Support Tool for Agile Software Development: An Empirical Evaluation

Data-driven effort estimation techniques of agile user stories: a systematic literature review

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