An Ant Colony Optimization Approach to the Software Release Planning Problem
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1 SBSE for Early Lifecyle Software Engineering 23 rd February 2011 London, UK An Ant Colony Optimization Approach to the Software Release Planning Problem with Dependent Requirements Jerffeson Teixeira de Souza, Ph.D Optimization in Software Engineering Group (GOES.UECE) State University of Ceará, Brazil
2 Nice to meet you, Jerffeson Teixeira de Souza, Ph.D. State University of Ceará, Brazil Professor
3 Our little time will be divided as follows Motivation Problem Definition Research Questions Problem Encoding The ACO Algorithm Experimental Evaluation Conclusion
4 Motivation The Search Based Software Engineering (SBSE) field has been benefited from a number of general search methods. Surprisingly, even with the large applicability and the significant results obtained by the Ant Colony Optimization (ACO) metaheuristic, very little has been done regarding the employment of this strategy to tackle software engineering problems modeled as optimization problems.
5 Ant Colony Optimization swarm intelligence framework, inspired by the behavior of ants during food search in nature. ACO mimics the indirect communication strategy employed by real ants mediated by pheromone trails, allowing óri individual ants to adapt their behavior to reflect the colony s search experience.
6 The software release planning problem addresses the selection and assignment of requirements to a sequence of releases, such that the most important and riskier requirements are anticipated, and both cost and precedence constraints are met.
7 The software release planning problem addresses the selection and assignment of requirements to a sequence of releases, such that the most important and riskier requirements are anticipated, and both cost and precedence constraints are met.
8 The software release planning problem addresses the selection and assignment of requirements to a sequence of releases, such that the most important and riskier requirements are anticipated, and both cost and precedence constraints are met.
9 The software release planning problem addresses the selection and assignment of requirements to a sequence of releases, such that the most important and riskier requirements are anticipated, and both cost and precedence constraints are met.
10 How can the ACO framework be adapted to solve the Software Release Planning problem in the presence of dependent requirements? ACO for the Software Release Planning problem
11 How can the ACO frameworkbe adapted to solve the Software Release Planning problem in the presence of dependent requirements? ACO for the Software Release Planning problem How does the proposed ACO adaptation compare to other metaheuristics in solving the Software Release Planning problem in the presence of dependent requirements? ACO versus Other Metaheuristics?
12 How can the ACO algorithm be adapted to solve the Software Release Planning problem in the presence of dependent requirements? ACO for the Software Release Planning problem THE ACO ALGORITHM
13 PROBLEM ENCONDING The problem will be encoded as a directed graph,, where, with representing mandatory moves, and representing optional ones. i. each vertex in represents a requirement ; ii. a directed mandatory edge, if ; iii. a directed optional edge, if and.
14 MORE PROBLEM ENCONDING if requirement has no precedent requirements and, for all unvisited requirements where
15 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) THE PROPOSED ACO ALGORITHM FOR THE SOFTWARE RELEASE PLANNING PROBLEM MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
16 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
17 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
18 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
19 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
20 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
21 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
22 OVERALL INITIALIZATION COUNT 1 MAIN LOOP REPEAT MAIN LOOP INITIALIZATION FOR ALL vertices r i V, visited i False FOR ALL vertices r i V, current_planning i 0 SINGLE RELEASE PLANNING LOOP // FINDS A NEW RELEASE PLANNING (current_planning) MAIN LOOP FINALIZATION IF current_planning.eval( ) > best_planning.eval( ) THEN best_planning current_planning COUNT ++ UNTIL COUNT > MAX_COUNT RETURN best_planning
23 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
24 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
25 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
26 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
27 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
28 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
29 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
30 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
31 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
32 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
33 // Besides r i, adds to release k all of its dependent requirements, and, repeatedly, their dependent requirements ADDS (r i, k) ENQUEUE (Q, r i ) WHILE Q DO r d DEQUEUE (Q) FOR EACH r s opt_vis k (i) DO ENQUEUE (Q, r s ) visited d True current_planning d k
34 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
35 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
36 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
37 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
38 SINGLE RELEASE PLANNING LOOP FOR EACH Release, k Randomly place ant k in a vertex r i V, where visited i False and overall_cost i budgetrelease k ADDS (r i, k) WHILE opt_vis k (i) 0 DO Move ant k to a vertex r j opt_vis k (i) with probability p k ij ADDS (r j, k) i j
39 EXPERIMENTAL EVALUATION RESULTS AND ANALYSES How does the proposed ACO adaptation compare to other metaheuristics in solving the Software Release Planning problem in the presence of dependent requirements? ACO versus Other Metaheuristics?
40 The Experimental Data Table below presents the number of releases, requirements and clients of the three synthetically generated instances used in the experiments. Instance Instance Features Name # Releases # Requirements # Clients INST.A INST.B INST.C
41 The Algorithms Genetic Algorithm (GA) widely applied evolutionary algorithm, inspired by Darwin s theory of natural selection, which simulates biological processes such as Inheritance, mutation, crossover, and selection Simulated Annealing (SA) it is a procedure for solving arbitrary optimization problems based on an analogy with the annealing process in solids.
42 Comparison Metrics Quality it relates to the quality of each generated solution, measured by the value of the objective function. Execution Time it measures the required execution time of each strategy.
43 RESULTS Instance GA SA ACO INST.A 8, ± , ± , ± INST.B 29, ± , ± 1, , ± INST.C 211, ± 3, , ± 8, , ± 2, Quality of Results for Instaces A, B anc C averages and standard deviations, over 100 executions
44 RESULTS Instance GA SA ACO INST.A ± ± ± INST.B 2, ± ± ± INST.C 125, ± , ± , ± Execution time (in milliseconds) for Instaces A, B anc C averages and standard deviations, over 100 executions
45 I N S T A Genetic Algorithm Simulated Annealing Ant Colony Optimization Boxplots showing maximum ( ), minimum ( ) and 25% - 75% quartile ranges of quality for all instances, for GA, SA and ACO.
46 Genetic Algorithm Simulated Annealing Ant Colony Optimization I N S T B Boxplots showing maximum ( ), minimum ( ) and 25% - 75% quartile ranges of quality for all instances, for GA, SA and ACO.
47 I N S T C Genetic Algorithm Simulated Annealing Ant Colony Optimization Boxplots showing maximum ( ), minimum ( ) and 25% - 75% quartile ranges of quality for all instances, for GA, SA and ACO.
48 Threats to Validity Small number, size and diversity of instances Artificial instances Parameterization of algorithms
49 Very little has been done regarding the employment of the Ant Colony Optimization (ACO) framework to tackle software engineering problems modeled as optimization problems. This talk described a novel ACO-based approach for the Software Release Planning problem with the presence of dependent requirement. All experimental results pointed out to the ability of the proposed ACO approach to generate precise solutions with very little computational effort. CONCLUSIONS
50 INVITATION II Brazilian Workshop on Optimization in Software Engineering along with XXV Brazilian Symposium on Software Engineering (SBES 2011) XV Brazilian Symposium on Programming Languages (SBLP 2011) XIV Brazilian Symposium on Formal Methods (SBMF 2011) V Brazilian Symposium on Software Components, Architectures and Reuse (SBCARS 2011) SÃO PAULO - SP, BRAZIL SEPTEMBER 26-30,
51 That is it! Thanks for your time and attention.
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