An Agile Effort Estimation Based on Story Points Using Machine Learning Techniques

Rao, Chitneni Prasad; Kumar, P. Siva; Sree, S. Rama; Devi, Johana

doi:10.1007/978-981-10-8228-3_20

Cited by 16 publications

(5 citation statements)

References 6 publications

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“…To improve the accuracy of Agile effort estimation, a combination of Neural Network was proposed by Panda et al (2015a;2015b) and Rao et al (2018). While Malgonde and Chari (2018) applied predictive algorithms with ensemble-based approaches that consisted of Support Vector Machine (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Decision Trees (DT).…”

Section: Conducting Phase Resultsmentioning

confidence: 99%

“…Non-hybrid E J Planning Poker (Chatzipetrou et al, 2018), (López-Mart´ınez et al, 2017a, (Lenarduzzi et al, 2015), (Mahnič & Hovelja, 2012), (Zahraoui & Idrissi, 2015) A Phase wise (Choudhari & Suman, 2012a), (Choudhari & Suman, 2012b) COSMIC (Desharnais et al, 2011 (Rao et al, 2018) Combination of Neural Networks (Bilgaiyan et al, 2018), (Panda et al, 2015a), (Panda et al, 2015b) Combination two deep learning (Choetkiertikul et al, 2018) Combination of Machine Learning such as SVM, SVR, ANN, KNN, and DT. (Abrahamsson et al, 2011), (Malgonde & Chari, 2018), (Zakrani et al, 2018) Table 9 Types of software effort estimation method and approach Complexity attribute is interpreted in different aspects, and most study understands it as the complexity of the project (Popli & Chauhan, 2014b;Garg & Gupta, 2015;Tanveer et al, 2016;Tanveer et al, 2017b;Bilgaiyan et al, 2018).…”

Section: Approach Methodsmentioning

confidence: 99%

“…Many studies used story point attribute that represent itself (Popli & Chauhan, 2014a;Hamouda, 2014;Panda et al, 2015a;Panda et al, 2015b;Khuat & Le, 2017;Zakrani et al, 2018;Rao et al, 2018). Meanwhile, others apply this to show baseline story-points, estimated story-points (ESP), and unadjusted value of story-points (Popli & Chauhan, 2014b).…”

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: Conducting Phase Resultsmentioning

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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“…Saini et al [74] employed a fuzzy logic model to estimate the effort of agile software projects. Effort estimation of agile projects using Machine Learning Techniques is presented in [69]. Dantas et al [24] presented a systematic literature review on effort estimation in ASD, including computing techniques.…”

Section: Related Workmentioning

confidence: 99%

Agile Effort Estimation: Comparing the Accuracy and Efficiency of Planning Poker, Bucket System, and Affinity Estimation Methods

Poženel,

Fürst,

Vavpotič

et al. 2023

Int. J. Soft. Eng. Knowl. Eng.

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Published studies on agile effort estimation predominantly focus on comparisons of the accuracy of different estimation methods, while efficiency comparisons, i.e. how much time the estimation methods consume was not in the forefront. However, for practical use in software development, the time required can be a very important cost factor for enterprises, especially when the accuracy of different agile effort estimations is similar. In this study, we thus try to advance the current standard accuracy comparison between methods by introducing efficiency, i.e. time it takes to use a method as an additional dimension of comparison. We conduct this comparison between three agile effort estimation methods that were not yet compared in the literature, namely, Planning Poker, Bucket System and Affinity Estimation. For the comparison, we used eight student teams with 29 students who had to use all the effort estimation methods during the course where they had to finish a programming project in 3 weeks. The results indicate that after the students get used to using the different methods the accuracy between them is not statistically significantly different, however, the efficiency is. On average, Bucket System and Affinity Estimation methods take half as much time as Planning Poker.

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“…The author advised carrying out more research utilizing the Fireworks Algorithm (FA), Random Forest, etc. [6].…”

Section: Related Workmentioning

confidence: 99%

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

Sunda,

Sinha

2023

IJRITCC

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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.

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An Agile Effort Estimation Based on Story Points Using Machine Learning Techniques

Cited by 16 publications

References 6 publications

A Review Article on Software Effort Estimation in Agile Methodology

A Review Article on Software Effort Estimation in Agile Methodology

Agile Effort Estimation: Comparing the Accuracy and Efficiency of Planning Poker, Bucket System, and Affinity Estimation Methods

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

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