Software project management net: a new methodology on software management
Software project management net: a new methodology on software management
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Software Engineering Economics
Software Engineering Economics
Is Existing Software Engineering Obsolete?
IEEE Software
A Net Practice for Software Project Management
IEEE Software
A genetic algorithm for resource-constrained scheduling
A genetic algorithm for resource-constrained scheduling
A policy-based resource instantiation mechanism to automate software process management
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Software project management with GAs
Information Sciences: an International Journal
Information and Software Technology
Analysis & recommendations for the management of cots: computer off the shelf-software projects
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Deriving evaluation metrics for applicability of genetic algorithms to optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Time-line based model for software project scheduling with genetic algorithms
Information and Software Technology
Optimized staffing for product releases and its application at Chartwell Technology
Journal of Software Maintenance and Evolution: Research and Practice - Search Based Software Engineering [SBSE]
Information and Software Technology
Simulating worst case scenarios and analyzing their combined effect in operational release planning
ICSP'08 Proceedings of the Software process, 2008 international conference on Making globally distributed software development a success story
Journal of Systems and Software
Evolutionary algorithms for the project scheduling problem: runtime analysis and improved design
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Solving software project scheduling problems with ant colony optimization
Computers and Operations Research
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
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The scheduling of tasks and the allocation of resource in medium to large-scale development projects is an extremely hard problem and is one of the principal challenges of project management due to its sheer complexity. As projects evolve any solutions, either optimal or near optimal, must be continuously scrutinized in order to adjust to changing conditions. Brute force exhaustive or branch-and-bound search methods cannot cope with the complexity inherent in finding satisfactory solutions to assist project managers. Most existing project management (PM) techniques, commercial PM tools, and research prototypes fall short in their computational capabilities and only provide passive project tracking and reporting aids. Project managers must make all major decisions based on their individual insights and experience, must build the project database to record such decisions and represent them as project nets, then use the tools to track progress, perform simple consistency checks, analyze the project net for critical paths, etc., and produce reports in various formats such as Gantt or Pert charts.Our research has developed a new technique based on genetic algorithms (GA) that automatically determines, using a programmable goal function, a near-optimal allocation of resources and resulting schedule that satisfies a given task structure and resource pool. We assumed that the estimated effort for each task is known a priori and can be obtained from any known estimation method such as COCOMO. Based on the results of these algorithms, the software manager will be able to assign tasks to staff in an optimal manner and predict the corresponding future status of the project, including an extensive analysis on the time-and-cost variations in the solution space. Our experiments utilized Wall's GALib as the search engine. The algorithms operated on a richer, refined version of project management networks derived from Chao's seminal work on GA-based Software Project Management Net (SPMnet). Generalizing the results of Chao's solution, the new GA algorithms can operate on much more complex scheduling networks involving multiple projects. They also can deal with more realistic programmatic and organizational assumptions. The results of the GA algorithm were evaluated using exhaustive search for five test cases. In these tests our GA showed strong scalability and simplicity. Its orthogonal genetic form and modularized heuristic functions are well suited for complex conditional optimization problems, of which project management is a typical example.