A State of the Art Regressor Model’s comparison for Effort Estimation of Agile software

2021 ◽  
Author(s):  
Mohit Arora ◽  
Abhishek Sharma ◽  
Sapna Katoch ◽  
Mehul Malviya ◽  
Shivali Chopra
Keyword(s):  
State Of The Art ◽  
Effort Estimation ◽  
Agile Software

Effort Estimation in Agile Software Development: An Updated Review

2018 ◽  
Vol 28 (11n12) ◽  
pp. 1811-1831 ◽  
Author(s):  
Emanuel Dantas ◽  
Mirko Perkusich ◽  
Ednaldo Dilorenzo ◽  
Danilo F. S. Santos ◽  
Hyggo Almeida ◽  
...  
Keyword(s):  
Software Development ◽  
State Of The Art ◽  
Agile Software Development ◽  
The State ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Cost Drivers ◽  
Machine Learning Methods ◽  
Agile Software

One of the main issues of an agile software project is how to accurately estimate development effort. In 2014, a Systematic Literature Review (SLR) regarding this subject was published. The authors concluded that there were several gaps in the literature, such as the low level of accuracy of the techniques and little consensus on appropriate cost drivers. The goal of our work is to provide an updated review of the state of the art based on this reference SLR work. We applied a Forward Snowballing approach, in which our seed set included the former SLR and its selected papers. We identified a strong indication of solutions based on Artificial Intelligence and Machine Learning methods for effort estimation in Agile Software Development (ASD). We also identified that there is a gap in terms of agreement on suitable cost drivers. Thus, we applied Thematic Analysis in the selected papers and identified a representative set of 10 cost drivers for effort estimation. This updated review of the state of the art resulted in 24 new relevant papers selected.

scholarly journals MINING EFFORT DATA FROM THE OSS REPOSITORY OF DEVELOPER’S BUG FIX ACTIVITY

2016 ◽  
Vol 3 (1) ◽  
pp. 107-128
Author(s):  
Syed Nadeem Ahsan ◽  
Muhammad Tanvir Afzal ◽  
Safdar Zaman ◽  
Christian Gütel ◽  
Franz Wotawa
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
State Of The Art ◽  
Tracking System ◽  
Previous History ◽  
Effort Estimation ◽  
Estimation Model ◽  
Activity Data ◽  
Actual Time ◽  
History Of

During the evolution of any software, efforts are made to fix bugs or to add new features in software. In software engineering, previous history of effort data is required to build an effort estimation model, which estimates the cost and complexity of any software. Therefore, the role of effort data is indispensable to build state-of-the-art effort estimation models. Most of the Open Source Software does not maintain any effort related information. Consequently there is no state-of-the-art effort estimation model for Open Source Software, whereas most of the existing effort models are for commercial software. In this paper we present an approach to build an effort estimation model for Open Source Software. For this purpose we suggest to mine effort data from the history of the developer’s bug fix activities. Our approach determines the actual time spend to fix a bug, and considers it as an estimated effort. Initially, we use the developer’s bug-fix-activity data to construct the developer’s activity log-book. The log-book is used to store the actual time elapsed to fix a bug. Subsequently, the log-book information is used to mine the bug fix effort data. Furthermore, the developer’s bug fix activity data is used to define three different measures for the developer’s contribution or expertise level. Finally, we used the bug-fix-activity data to visualize the developer’s collaborations and the involved source files. In order to perform an experiment we selected the Mozilla open source project and downloaded 93,607 bug reports from the Mozilla project bug tracking system i.e., Bugzilla. We also downloaded the available CVS-log data from the Mozilla project repository. In this study we reveal that in case of Mozilla only 4.9% developers have been involved in fixing 71.5% of the reported bugs.

scholarly journals Effort Estimation in Agile Software Development: an Updated Review

2018 ◽  
Author(s):  
Emanuel Dantas Filho ◽  
Mirko Perkusich ◽  
Ednaldo Dilorenzo ◽  
Danilo Santos ◽  
Hyggo Almeida ◽  
...  
Keyword(s):  
Software Development ◽  
Agile Software Development ◽  
Effort Estimation ◽  
Agile Software

scholarly journals Estimation Approaches of Machine Learning in Scrum Projects: A Review

2021 ◽  
Vol 9 (11) ◽  
pp. 1110-1118
Author(s):  
Chitrak Vimalbhai Dave
Keyword(s):  
Machine Learning ◽  
Regression Testing ◽  
Process Models ◽  
Effort Estimation ◽  
Software Projects ◽  
It Industry ◽  
Resistance Factors ◽  
User Story ◽  
Inaccurate Estimation ◽  
Agile Software

Abstract: It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx 43% of the projects are often delivered late and entered crises because of over budget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g. Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon forstrengthening the collaboration betweendeveloper and customer.Estimation has always been challenging in Agile as requirements are volatile. This encourages researchersto work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paperreviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation. Keywords: Machine Learning, Scrum, Scrum Projects, Effort Estimation, Agile Software Development

scholarly journals Current Research Trend on Agile Software Development

2019 ◽  
pp. 99-105
Author(s):  
Handrie Noprisson
Keyword(s):  
Literature Review ◽  
Software Development ◽  
Systematic Literature Review ◽  
Traditional Approach ◽  
Agile Software Development ◽  
Research Trend ◽  
Effort Estimation ◽  
Software Methodology ◽  
Meta Analyses ◽  
Agile Software

In recent years, the software development methodology evolves from the traditional approach to agile software development. This paper attempted to conduct a systematic literature review (SLR) regarding the improved agile software development to tackle its weakness based on recent research papers. Systematic Reviews and Meta-Analyses (PRISMA) as Systematic Literature Review Method (SLR). SLR is the review method which uses some protocols in order to minimize bias in the reviews. The improved of agile software methodology mostly regarding code reusability, usability, project quality, estimation, software delivery, usability, user responses and requirements delivery, communication between members, usability, practical activities, communication between team and stake holder, usability, workflow (learning), problem identification and effort estimation.

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