Search-based techniques applied to optimization of project planning for a massive maintenance project

This paper evaluates the use of three different search-based techniques, namely genetic algorithms, hill climbing and simulated annealing, and two problem representations, for planning resource allocation in large massive maintenance projects. In particular the search-based approach aims to find an optimal or near optimal order in which to allocate work packages to programming teams, in order to minimize the project duration.The approach is validated by an empirical study of a large, commercial Y2K massive maintenance project, which compares these techniques with each other and with a random search (to provide base line comparison data).Results show that an ordering-based genome encoding (with tailored cross over operator) and the genetic algorithm appear to provide the most robust solution, though the hill climbing approach also performs well. The best search technique results reduce the project duration by as much as 50%

Search algorithms for regression test case prioritization

Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritisation techniques aim to improve the effectiveness of regression testing, by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritisation has focused on Greedy Algorithms. However, it is known that these algorithms may produce sub-optimal results, because they may construct results that denote only local minima within the search space. By contrast, meta-heuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, meta-heuristic and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for 3 choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterisation of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multi-modal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multi-modal nature of the landscape

Scheduling Refactoring Opportunities Using Computational Search

Maintaining a high-level code quality can be extremely expensive since time and monetary pressures force programmers to neglect improving the quality of their source code. Refactoring is an extremely important solution to reduce and manage the growing complexity of software systems. Developers often need to make trade-offs between code quality, available resources and delivering a product on time, and such management support is beyond the scope and capability of existing refactoring engines. The problem of finding the optimal sequence in which the refactoring opportunities, such as bad smells, should be ordered is rarely studied. Due to the large number of possible scheduling solutions to explore, software engineers cannot manually find an optimal sequence of refactoring opportunities that may reduce the effort and time required to efficiently improve the quality of software systems. In this paper, we use bi-level multi-objective optimization to the refactoring opportunities management problem. The upper level generates a population of solutions where each solution is defined as an ordered list of code smells to fix which maximize the benefits in terms of quality improvements and minimize the cost in terms of number of refactorings to apply. The lower level finds the best sequence of refactorings that fixes the maximum number of code smells with a minimum number of refactorings for each solution (code smells sequence) in the upper level. The statistical analysis of our experiments over 30 runs on 6 open source systems and 1 industrial project shows a significant reduction in effort and better improvements of quality when compared to state-of-art bad smells prioritization techniques. The manual evaluation performed by software engineers also confirms the relevance of our refactoring opportunities scheduling solutions.Master of ScienceComputer Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136063/1/Scheduling Refactoring Opportunities Using Computational Search.pd

Developing dynamic maximal covering location problem considering capacitated facilities and solving it using hill climbing and genetic algorithm

The maximal covering location problem maximizes the total number of demands served within a maximal service distance given a fixed number of facilities or budget constraints. Most research papers have considered this maximal covering location problem in only one period of time. In a dynamic version of maximal covering location problems, finding an optimal location of P facilities in T periods is the main concern. In this paper, by considering the constraints on the minimum or maximum number of facilities in each period and imposing the capacity constraint, a dynamic maximal covering location problem is developed and two related models (A, B) are proposed. Thirty sample problems are generated randomly for testing each model. In addition, Lingo 8.0 is used to find exact solutions, and heuristic and meta-heuristic approaches, such as hill climbing and genetic algorithms, are employed to solve the proposed models. Lingo is able to determine the solution in a reasonable time only for small-size problems. In both models, hill climbing has a good ability to find the objective bound. In model A, the genetic algorithm is superior to hill climbing in terms of computational time. In model B, compared to the genetic algorithm, hill climbing achieves better results in a shorter time

The Effect of Communication Overhead on Software Maintenance Project Staffing: a Search-Based Approach

Brooks ’ milestone ‘Mythical Man Month ’ established the observation that there is no simple conversion between peo-ple and time in large scale software projects. Communica-tion and training overheads yield a subtle and variable re-lationship between the person-months required for a project and the number of people needed to complete the task within a given time frame. This paper formalises several instantiations of Brooks’ law and uses these to construct project schedule and staffing instances — using a search-based project staffing and scheduling approach — on data from two large real world maintenance projects. The results reveal the impact of dif-ferent formulations of Brooks ’ law on project completion time and on staff distribution across teams, and the influ-ence of other factors such as the presence of dependen-cies between work packages on the effect of communication overhead

The relationship between search based software engineering and predictive modeling

Search Based Software Engineering (SBSE) is an approach to software engineering in which search based optimization algorithms are used to identify optimal or near optimal solutions and to yield insight. SBSE techniques can cater for multiple, possibly competing objectives and/or constraints and applications where the potential solution space is large and complex. This paper will provide a brief overview of SBSE, explaining some of the ways in which it has already been applied to construction of predictive models. There is a mutually beneficial relationship between predictive models and SBSE. The paper sets out eleven open problem areas for Search Based Predictive Modeling and describes how predictive models also have role to play in improving SBSE

The use of search-based optimization techniques to schedule and staff software projects: an approach and an empirical study

Allocating resources to a software project and assigning tasks to teams constitute crucial activities that affect project cost and completion time. Finding a solution for such a problem is NP-hard; this requires managers to be supported by proper tools in performing such an allocation. This paper shows how search-based optimization techniques can be combined with a queuing simulation model to address these problems. The obtained staff and task allocations aim to minimize the completion time and reduce schedule fragmentation. The proposed approach allows project managers to run multiple simulations, compare results and consider trade-offs between increasing the staffing level and anticipating the project completion date and between reducing the fragmentation and accepting project delays. The paper presents results from the application of the proposed search-based project planning approach to data obtained from two large scale commercial software maintenance projects