A History-Based Automatic Scheduling Model for Personnel Risk Management

Abstract

Personnel risk is an issue which has not been researched well but plays an important role to determine whether a software project succeeds or fails. Most existing research work focuses on subjective expertise while an objective view is lacking. Furthermore, to the best of our knowledge, the demand for an automatic tool to support risk management has not been answered yet. In this research, based on objective historical data, we extend our earlier model, cabilitybased scheduling framework, by including risk analysis. Hence we provide a novel approach to mitigate personnel risks while achieving a project schedule with minimum cost.

A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling

“…Thus, to mutate at this range may be an appropriate choice. Besides, we can also see that the mutation range from "0-22" to "14-22" bits does not lead to a significant improvement, and some ranges are even slightly fluctuant (e.g., "6-22" in the GGO model or " [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]" in the GO model). Consequently, mutation on these lower bits may also be uninfluential.…”

“…The crossover may happen at different bits with a probability called crossover rate, P cross . This rate typically ranges from 0.5 to 0.8 from GA literatures [16,22]. We decide to adopt uniform crossover in our experiments.…”

“…Then based on the IEEE floating-point standard, we can easily decode the best chromosomes from parametric solutions. Fitness function is an indicator of the fitness of every chromosome that shows how close this chromosome is to the desired solution [16]. MGA selects the candidate chromosomes from the current population based on their fitness values.…”

“…In the field of software engineering or in its sub-area software reliability, GA has also attracted the attention of researchers with solving the optimization to their specific problem domains [1,6], such as optimal scheduling [16], optimal test data generation [21], optimal module clustering [3], etc. In the past, Huang and Chiu [12,13] investigated the estimated accuracy of adopting GA to determine weighted similarity measures in either analogy-based software effort estimation models or grey relational analysis models.…”

Search-Based Software Project Management

“…The effects of overruns are not immediately obvious, since they can affect the critical path, making previously less important work packages become more important for the overall project completion time. Jiang et al (2007) proposed an approach that extracts personnel risk information from historical data and integrates risk analysis into project scheduling performed with GA. A rescheduling mechanism is designed to detect and mitigate potential risks along with the software project development. However, the proposed approach has not been empirically validated.…”