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Due to their expanded methods, agile methodologies are now the accepted standard for software development. Collaboration, addressing changing requirements with workable software, and simple design are just a few aspects of typical techniques. By employing the aforementioned approaches, these methods handle the issue of variable requirements. As a result, these methods lower the cost of changes at a later point in the software development process. Due to the unpredictability of requirements, such methodologies do not hold up well for an early estimate of size, cost, and timeframe. Agile techniques, it has been noticed, rely on expert analysis and prior project information to estimate cost, complexity, and time. Such approaches, it has been discovered, do not take into account the critical aspects affecting the project's cost, complexity, and timeframe when estimating. Based on predefined estimation methods including analogy and planning, poker becomes unexpected in the lack of historical data and professionals. As a result, there is a pressing need to develop a simple computational solution that takes into account the factors that influence the project budget, complexity, and timeframe. It also serves as a foundation for unskilled practitioners to make better estimates. The research of both traditional and agile estimation techniques with a comparability of concepts and variations is presented in this work. We looked into a few key elements that influence the estimation of an agile project with lower, moderate, and higher scalability factors. Integrating critical aspects is also recommended using the Constructive Agile Estimation Algorithm (CAEA). [ABSTRACT FROM AUTHOR]

Risk management is considered as a critical project management activity that needs to be performed for successful software development. Within risk management, risk prioritization is an important process which helps the software team to effectively manage the risks at early stage of the project. In agile-based software environment, it is necessary to prioritize the risks in an effective manner in order to address the risks in shorter duration of time. In recent times, swarm intelligence techniques are widely popular in solving various optimization problems in software development process. The main reason is due to its convergence accuracy toward global optimal solution and faster computational time. In this study, an efficient risk prioritization technique termed as ARP–GWO (agile risk prioritization–grey wolf optimization) has been proposed for prioritizing the risk factors present in the agile software development using grey wolf optimization (GWO). The proposed ARP–GWO method helps the organization to mitigate the risks and ensures successful delivery of software products with good quality, in lesser cost and time. The effectiveness of ARP–GWO is analyzed using two performance metrics, namely Index of Integration and Usability Goals Achievement Metric, for which case studies are performed on five industrial projects from two different organizations. The experimental results indicate that ARP–GWO is most effective in prioritization of risks and offers better enhancement with high degree of satisfaction among developers and users as compared with the existing agile process. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) algorithm inspired by the complex behaviors of honey bees in foraging is one of the most significant swarm intelligence-based meta-heuristics and has been successfully applied to a number of numerical and combinatorial optimization problems. In this study, for increasing the early convergence performance of the ABC algorithm while protecting the qualities of the final solutions, a new exploitation mechanism from the best food source that is managed by the number of evaluations is described and its efficiency on both employed and onlooker bee phases is analyzed. The results of the experimental studies obtained from a set of benchmark problems showed that the ABC algorithm with the proposed method performs significantly better than the standard implementation of ABC algorithm and its other variants in terms of convergence speed and solution quality especially for the difficult problems that should be solved before completion of the relatively small number of fitness evaluations. [ABSTRACT FROM AUTHOR]

In agile methods, software products are developed in several releases. In each release, a new set of requirements for development is proposed. Due to technical and non-technical problems, it is almost impossible to develop all the proposed requirements in the next release. The select of an optimal subset from among all the proposed requirements has become an important problem for the developer team. The aim of this problem is to select an optimal subset from among the requirements for product development in the next release so that it has the highest satisfaction to the clients and the lowest cost for the manufacturing company. Since this problem faces two conflicting objectives and several constraints, it is placed in the NP-hard problems category. In this paper, we intend to formulate this problem for the first time as a fuzzy multi-objective optimization problem. We intend to use an artificial chemical reaction optimization algorithm to solve this problem. In the implementation stage, we make use of five interactions between requirements as one of the constraints of the problem for the first time. Two randomized fuzzy synthetic datasets are used to do the experiments. The results of the proposed algorithm are evaluated using three criteria of multi-objective problems. The results and diagrams of the proposed algorithm are very reliable and can help the developer team to make a decision. [ABSTRACT FROM AUTHOR]