Constraint‐based human resource allocation in software projects

Abstract

Resource allocation in a software project is crucial for successful software development. Among various types of resources, human resource is the most important as software development is a human‐intensive activity. Human resource allocation is very complex owing to the human characteristics of developers. The human characteristics affecting allocation can be grouped into individual‐level characteristics and team‐level characteristics. At the individual level, familiarity with tasks needs to be taken into account as it affects the performance of developers. In addition, developers have different levels of productivity, depending on their capability and experience; the productivity of developers also varies according to tasks. At the team level, characteristics such as team cohesion, communication overhead, and collaboration and management also affect human resource allocation. As these characteristics affect the efficiency of project execution, we treat them as constraints of human resource allocation in our approach. We identify individual‐level constraints and team‐level constraints based on the literature and interviews with experts in the industry. With these constraints, our approach optimizes the scheduling of human resource allocations, resulting in more realistic and efficient allocations. We also provide a guideline supporting various factors, with respect to roles and module characteristics, to estimate the productivity of developers based on COCOMO II. As productivity data are hard to obtain and manage, our guideline can provide a useful direction for human resource allocation in case of software projects. To validate our proposed approach, we document a case study using real project data. Copyright © 2011 John Wiley & Sons, Ltd.

Benchmarking and Comparison of Software Project Human Resource Allocation Optimization Approaches

“…52 approaches were considered by this study. The comparison shows that few approaches among them all are satisfying the criteria adopted and capable for the illustrated aspects by this study as the one in [7]. Skills and productivity of resources found are the least aspects considered by the approaches used by Pixoto et al [4].…”

“…The first results we present are for the Class 1 approaches [10], [11], and Kang et al [7]. The dataset used is the Class One dataset, which only considers tasks, resources and availability, and has an optimal solution of 80.33 for its project schedule.…”

“…Each task in the dataset moreover has the value of dependency attribute represented as the task number that the task is depends on. Productivity of a resource is represented by the same metric used by [7]. A resource can be productive as a normal person, which is equal to 1, or twice the normal person represented by 2.…”

“…The approaches have been classified according to the SSSP classes introduced in Section 3.2. The optimization techniques used by the approaches are Genetic Algorithms (GA) by [5,6,10,11], and a modified version of Simulated Annealing (SA) called Accelerated SA by [7]. Both techniques are belong to the same search algorithm class called meta-heuristic.…”

A Mathematical Framework for Crisis Spatial Crowdsourcing Services

“…Research affairs have covered different approaches such as simulation, genetic algorithms, expert systems and fuzzy theory (Mota et al, 2009), (Daher and Almeida, 2010), (Kang et al, 2011). They determine a fit between candidates' set of skills and skills which are required for a given tasks.…”