A new evolutionism based self‐adaptive multi‐objective optimization method to predict software cost estimation

Abstract

One of the major challenge that organizations face in the present environment is having an efficient model for software cost estimation (SCE). In this article, the significance of the meta‐heuristic algorithm in addressing various optimization challenges faced in mathematical models and software applications is discussed. The proposed method uses the new evolutionism‐based self‐adaptive mutation operator to solve the multi‐objective optimization problems. This approach addresses the issues that exist in multi‐objective differential evolution algorithms. To improve diversity among candidate solutions, the Pareto optimality principle is integrated with the evolutionism‐based self‐adaptive mutation operator in a multi‐objective DE algorithm. To reduce the time complexity of Pareto dominance, we have adopted the non‐dominated sorting algorithm. We used eight benchmark test functions to evaluate the effectiveness of the proposed method, and it outperformed the most recent multi‐objective evolutionary algorithms (MOEAs). Furthermore, this article explores software engineering problems like SCE by using the proposed approach, where SCEs are accurately predicted by optimizing the tuning parameters of the multi‐objective constructive cost model. The proposed algorithm achieves better cost prediction as compared to the other standard benchmark algorithms for all objective problems in terms of prediction, mean absolute error, and root mean square error.

A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation

“…In Gouda and Mehta [20], the authors discuss the value of the meta-heuristic algorithms in tackling numerous optimization problems that arise in software applications and mathematical models. The novel evolutionism-based selfadaptive mutation operator is used in the proposed approach to address multi-objective optimization issues.…”

Automatic refactoring of conditions and substitutions for B state transition models

“…Example 8. The value of T is given by Equation (3). By collecting all reachable states in T, we have All-States(T) ≡ S ← {(1, 1), (1, 2), (2, 2), (2, 3), (3, 3), (3,4), (4, 4), (4, 5), (5, 5), (5,6), (6, 6)}.…”

“…that is true on both (1, 2) and (3,4). As U becomes an empty set by removing the two states, the loop terminates.…”

“…There have been major advances in Software Engineering (SE) techniques with the advent of Artificial Intelligence (AI) in recent years. For example, automated software development is becoming a trend of SE, ranging from the estimation of development workload and cost, [1][2][3] to program generation and evolution. [4][5][6][7][8] Repair (APR) 9 is a nontrivial task as it concerns not only program diagnosis, which can be achieved by automated testing and verification, but also patch synthesis, which generates repairs from program diagnosis results.…”