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Journal of Systems and Software
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Example publications from this periodical
The following articles are from "Journal of Systems and Software":
Verner, June M. and Cerpa, Narciso (1997): Prototyping: Does your Perception Depend on Your Job?. In Journal of Systems and Software, 36 (1) pp. 3-16.
Sutcliffe, Alistair G. (2000): Domain analysis for software reuse. In Journal of Systems and Software, 50 (3) pp. 175-199. Available online
Sutcliffe, Alistair G., Papamargaritis, George and Zhao, Liping (2006): Comparing requirements analysis methods for developing reusable component libraries. In Journal of Systems and Software, 79 (2) pp. 273-289. Available online
Hoorn, Johan F. and Konijn, Elly A. (2007): Requirements change: Fears dictate the must haves; desires the won’t haves. In Journal of Systems and Software, 80 (3) pp. 328-355.
Pereira, Javier, Cerpa, Narciso, Verner, June, Rivas, Mario and Procaccino, Drew (2008): What Do Software Practitioners Really Think About Project Success: A Cross-Cultural Comparison. In Journal of Systems and Software, 81 (6) pp. 897-907.
Due to the increasing globalization of software development we are interested to discover if there exist significant cultural differences
in practitioners' definition of a successful software project. This study presents the results of a survey in which Chilean software practitioners'
perceptions of project success are compared with previous research with US practitioners. Responses from both groups of practitioners
indicate that there is a relationship between team-work and success; our results also indicate that there are similar perceptions
related to the importance of job satisfaction and project success. However, Chilean responses suggest that if a practitioner is allowed too
much freedom within the work environment, job stress results; this in turn is reflected in increasing demands for both job satisfaction and
good environmental conditions. This may indicate the potential for the attribution of failure to conditions outside the team, thus preventing
a search for problematic team issues and technical problems. In contrast, the data suggests peer control inside the US teams
indicating a less stressful environment.
© All rights reserved Pereira et al. and/or Elsevier
Reyes, Francisco, Cerpa, Narciso, Candia, Alfredo and Bardeen, Matthew (2011): The optimization of success probability for software projects using genetic algorithms. In Journal of Systems and Software, 84 (5) pp. 775-785.
The software development process is usually affected by many risk factors that may cause the loss of control and failure, thus which need to be identified and mitigated by project managers. Software development companies are currently improving their process by adopting internationally accepted practices, with the aim of avoiding risks and demonstrating the quality of their work.
This paper aims to develop a method to identify which risk factors are more influential in determining project outcome. This method must also propose a cost effective investment of project resources to
improve the probability of project success.
To achieve these aims, we use the probability of success relative to cost to calculate the efficiency of the probable project outcome. The definition of efficiency used in this paper was proposed by researchers in the field of education. We then use this efficiency as the fitness function in an optimization technique based on genetic algorithms. This method maximizes the success probability output of a prediction model relative to cost.
The optimization method was tested with several software risk prediction models that have been developed based on the literature and using data from a survey which collected information from inhouse
and outsourced software development projects in the Chilean software industry. These models predict the probability of success of a project based on the activities undertaken by the project manager
and development team. The results show that the proposed method is very useful to identify those activities needing greater allocation of resources, and which of these will have a higher impact on the
projects success probability.
Therefore using the measure of efficiency has allowed a modular approach to identify those activities in software development on which to focus the project's limited resources to improve its probability of success. The genetic algorithm and the measure of efficiency presented in this paper permit model independence, in both prediction of success and cost evaluation.
© All rights reserved Reyes et al. and/or Elsevier
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Jul 24th, 2014
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