Search

• 'Agile cost estimation in scrum development. • Cost estimation methods in scrum and limitations. • Suggested a cost estimation model. Agile has been invented to improve and overcome the deficiencies of efficient software development. At present, the agile model is used in software development vastly due to its support to both developers and clients resourcefully. Agile methodology increases the interaction between the developer and client to make the software product defect-free. The agile model is getting to be a well-known life cycle model because of its particular features and most owing is to allow changes at any level of the project from the product owner. However, on other hand, this novel feature is a disadvantage of the agile model due to frequent change requests from the client has increased the cost and time. To overcome cost and time estimation issues different cost estimation techniques are being used in agile development but no one is pertinent for accurate estimation. Therefore, this study has proposed a cost estimation technique. The proposed estimation technique is predictions-based and has different categorizations of projects based on user stories complexities and the developer's expertise. We have applied the suggested technique to ongoing projects to find the results and effectiveness. We have used two projects with different sizes and user stories. Both projects have different modules and developers with different expertise. We have used the proposed estimation technique on projects and done a survey session with the teams. This survey session's main objective is to reveal the statistical findings of the proposed solution. We have designed the 12 hypotheses for statistical analysis. [ABSTRACT FROM AUTHOR]

• In this article, all 36 dimensional success factors are considered to estimate the required effort and cost to develop any Agile-Scrum based software project. • The work presented in the article computes the required time, effort and cost of projects mentioned in a data set on the basis of different inputs to the system. • For computation purposes, we divided the projects into three categories as Low, Medium and High level project for the unbiased estimation. • The datasets considered for computation purposes, also validated by experienced professionals from software industries. • It is found that the results obtained from the extended algorithm are cost-effective and less effort-oriented to a great extent as a compared to the existing algorithm. Effort and cost estimation is essential processes during software development. For agile methodologies as well, several approaches have been proposed so far to estimate the required effort and cost to develop any software system. Genuine assessment of software products is not an easy task as it requires the constant attention of the product owner. For effective estimation, we need to consider various success factors associated with all possible dimensions. In this article, we look at Scrum-based Agile projects that are developed over multiple sprints. We proposed an extension to the existing algorithm based on a total of 36 success factors; Which estimates the development effort and cost required to complete the project. For assessment and calculation, we have taken a data-set of a total of 30 projects categorized into three groups as low, medium, and high-level projects. This dataset is further validated by experienced professionals. We also compared our results with the existing approach and found that our results are cost-effective and require less effort even after considering more success factors. [ABSTRACT FROM AUTHOR]

• Software scrum development. • Software cost estimation is still a complex task. • Effort estimation techniques in scrum still has some flaws that need to be resolve. Scrum methodology is the most useful and adopted part of the agile methodology. Many organizations are adopting Scrum for the last decade to meet their software requirements. It is facilitating the software industries to develop software applications according to the user's requirements. Scrum helps to fulfill the customer's requirements by interacting with the client and developer. Instead of this, scrum still has some challenges that influence the cost of a scrum. These challenging factors include coordination, team size, change request, complexity, and issues in daily meeting sessions. Due to these factors mostly the cost and time exceed scrum-based projects. To overcome these issues from the accurate and precise estimation in the scrum project we have developed a web-based software system to estimate the effort of a user story as well as the budget. There are some sophisticated cost estimation techniques for scrum-based projects. But all estimation techniques have some limitations that make them less useful for estimation and overcoming the effects of these factors in the scrum. We have surveyed the multiple software industries to get the static findings of the survey. We have designed the 30 hypotheses that main aim to reveal how much the proposed estimation technique is valuable for the accuracy of estimation. For the inclusion of participants, we have set the age criteria to get the opinions of the most experienced programmers. The main aim of this paper is to provide knowledge about key challenging factors in Scrum that cause exceed in cost and time, and also briefly explain why existing scrum cost estimation techniques are not pertinent for projects. In the last, we propose a framework as a solution to manage these factors and control cost and time from exceeding. We also adopted this estimation technique in different case studies in software industries for findings and results. The technique calculates the efforts autonomously. We design a software-based estimation technique that collects inputs from team/s and estimates the cost and time of a project. In outcomes, we found that our cost estimation approach reduced the issues from scrum software project development and make it more proficient for estimation of projects. Our proposed estimation technique introduced the new concept of estimation that facilitate the client, software industry, and developers as well to meet the requirements of the client, fulfill the changes during the sprints/s development and deliver the project within the time and cost. [ABSTRACT FROM AUTHOR]

• In this paper, the Fuzzy TOPSIS method has been applied to identify the most suitable SDLC model for software development in the present scenario. • The work presented in the article computes the ranks and closeness coefficients of all the alternative SDLC models based on different criteria. • Six widely accepted SDLC models were compared with their core features by applying fuzzy logic and decision support systems in the form of the Multi-Criteria Decision Making method. • Five experienced decision-makers from software industries, assigned weights in linguistic terms to each alternative model against criteria. • By applying the Fuzzy TOPSIS method, it is validated that Agile Methodology is an Ideal selection for software development processes. Agile methodologies have been an emerging choice of software professionals for the past decade and a half. However, apart from this, some other SDLC models are also available for selection in front of software developers to develop any software. Usually, project managers select any of these models to develop software through their past experiences. There is no logical basis for this selection to be completely correct, as a result of which there is always a risk of software failure or over budget if an inappropriate model has opted. Keeping this problem of software industries in mind, an ideal SDLC model has been identified mathematically in this article. In this article, we applied the Fuzzy TOPSIS method that validates Agile software development as an ideal choice. We have taken a total of six software development processes that are being applied globally. Feedback from five experienced decision-makers has been taken in the form of linguistic terms and further converted into fuzzy values to perform the computation of the closeness coefficient rank of each experimented alternative software development process. [ABSTRACT FROM AUTHOR]