Estimating User Stories’ Complexity and Importance in Scrum with Bayesian Networks

López-Martínez, Janeth; Juárez‐Ramírez, Reyes; Ramírez‐Noriega, Alan; Licea, Guillermo; Navarro-Almanza, Raúl

doi:10.1007/978-3-319-56535-4_21

Cited by 7 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although Planning Poker has a lot of benefits, the result was relying on observation experts. To overcome these issues, López-Martínez et al (2017b) proposed a new model to improve Planning Poker by estimating the complexity and importance of user stories using Bayesian Network. Even though it showed good results primarily to facilitate newbie developers in deciding when to estimate user stories, this model still needed expert knowledge to build a Bayesian Network.…”

Section: Conducting Phase Resultsmentioning

confidence: 99%

“…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%

See 3 more Smart Citations

A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

View full text Add to dashboard Cite

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.

show abstract

Section: Conducting Phase Resultsmentioning

confidence: 99%

Section: Approach Methodsmentioning

confidence: 99%

Section: Approach Methodsmentioning

confidence: 99%

Section: Approach Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

View full text Add to dashboard Cite

show abstract

“…Zahraoui et al [46] adjusted story points using: priority, size, and complexity. The complexity and importance of a user story were considered by Lopez et al [20], while Dragicevic et al [11] considered the skills of the developers and requirements complexity. These approaches use historical data and expert knowledge to estimate effort with better accuracy.…”

Section: Background and Related Workmentioning

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

View full text Add to dashboard Cite

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.

show abstract

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

Alsaadi

Saeedi

2022

Artif Intell Rev

View full text Add to dashboard Cite

Estimating User Stories’ Complexity and Importance in Scrum with Bayesian Networks

Cited by 7 publications

References 9 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

Contact Info

Product

Resources

About