A Multi-criteria Decision Making Approach for Resource Allocation in Software Engineering

Abstract

The completion of reliable software products within the expected time frame represents a major problem for companies that develop software applications. As the field grows, the software industry continues to struggle with delivering products in a timely manner. A major cause for increased cost and low quality in software products can be attributed to inadequate resource allocation. Therefore, it is important to develop systematic personnel assignment processes that consider the complete candidate skill set and provide the best fit to increase quality, reduce cost, and reduce training time. This paper presents a novel methodology that considers multiple project-specific skills to assign resources to software tasks. The methodology takes into account the existing capabilities of candidates to determine the best fit based on the required skills for the task. A sample case study is used to show the methodology's capabilities.

Value based Incremental Software Development

“…For example, [8] used this approach for the construction of a rubber compound for tire treads. [9] applied desirability functions for personnel task assignment that considers multiple candidate skills set. Yi j > T Equation 3 and 4 represents the maximization and mini mization case respectively that were proposed by [8] and used by [9] in personnel task assignment.…”

“…r is a vector that represents the importance of each candidate MMF in scale 0-10 (for this paper, the value of r would remain 1 for linear desirability functions. see [8] and [9] for more detail). Dij is the resultant matrix that can be generated after applying minimization and maximization formulas.…”

“…Candidate MMF having higher score is elected for development. Equation 5 [9] is the formula for calculating geometric mean where m represents the geometric values for MMFs 1 to n. …”

A Multi-Criteria Evaluation of Information Security Controls Using Boolean Features

“…They have been used extensively in the literature for process optimization in industrial settings, where finding a set of operating conditions that optimize all responses for a particular system is desired [18], [21]. Through Desirability Functions, each system response y i is converted into an individual function d i that varies over the range 0 ≤ d i ≤ 1, where d i = 1 when a goal is met, and d i = 0 otherwise [20].…”

Untitled

“…Desirability Functions are a popular approach for simultaneous optimization of multiple responses [19], [20]. They have been used extensively in the literature for process optimization in industrial settings, where finding a set of operating conditions that optimize all responses for a particular system is desired [18], [21]. Through Desirability Functions, each system response y i is converted into an individual function d i that varies over the range 0 ≤ d i ≤ 1, where d i = 1 when a goal is met, and d i = 0 otherwise [20].…”