Using ant colony optimization in software development project scheduling

Abstract

Resource allocation and tasks assignment to software development teams are very crucial and arduous activities that can affect a project's cost and completion time. Solution for such problem is NP-Hard and requires software managers to be supported with efficient tools that can perform such allocation and can resolve the software development project scheduling problem (SDPSP) more efficiently. Ant colony optimization (ACO) is a rapidly evolving meta-heuristic technique based on the real life behavior of ants and can be used to solve NP-Hard (SDPSP) problem. Different versions of ACO meta-heuristic have already been applied to the software project scheduling problem in the past that took various resources into account. We have applied elitist strategy of ACO (elitist ant system) for solving SDPSP in a parameter-constrained environment taking project's cost and duration into consideration. The objective of the ACO-SDPSP methodology allows software project managers and schedulers to assign most effective set of employees that can contribute in minimizing cost and duration of the software project. Experimental results show that the proposed ACO-SDPSP methodology is promising in achieving the desired results.Index Terms -Ant colony optimization, Software project management, Software development project scheduling. 978-1-4673-6217-7/13/$31.00 c 2013 IEEE

Using Static Information of Programs to Partition the Input Domain in Search-based Test Data Generation

“…Moreover, Cohen et al used SA in order to generate test data in combinatorial testing [13]. Sine ACO has shown notable results in solving optimization problems [14][15] [16], some scholars have utilized it to resolve software engineering problems in a wide range of sub-fields such as software project scheduling [17], release planning optimization [18], software quality prediction [19], and software testing [20]. Lam et al [21] and Srivastava et al [22] utilized ACO to generate test sequences for state-based software testing.…”