Exploring iterative and incremental approaches, current software projects define various delivery releases, which ought to be developed within budgetary constraints, but raising customers' satisfaction as much as ...
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Exploring iterative and incremental approaches, current software projects define various delivery releases, which ought to be developed within budgetary constraints, but raising customers' satisfaction as much as possible. In search based software engineering, such a problem is referred to as next release problem (NRP), in which the selection of software requirements for the next release is reformulated as an optimization problem. More recently, NRP proposals have evolved to a multi-objective perspective, providing non-dominated recommendations that balance the trade-off surface with respect to customers' satisfaction and development cost. Despite key advancements in NRP proposals, most of them do not deal with software risks, which represent a vital aspect that can deeply impact on project cost and customers' satisfaction. In such a direction, using multi-objective evolutionary algorithms (MOEAs), this paper proposes and evaluates a risk-driven systematic approach for the NRP problem, in which a risk analysis is incorporated for estimating the impact of software risks in development cost and customers' satisfaction. By contrasting three different well-known MOEAs, empirical results based on two semi-real datasets reveal the potential efficiency and practical applicability, bringing not only more realistic, precise and meaningful estimations, but also finding a significant percentage of unanticipated solutions.
multi-objectiveevolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-ofthe-art SMTIBEA algorithm extends...
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multi-objectiveevolutionary algorithm (MOEA) has been widely applied to software product lines (SPLs) for addressing the configuration optimization problems. For example, the state-ofthe-art SMTIBEA algorithm extends the constraint expressiveness and supports richer constraints to better address these problems. However, it just works better than the competitor for four out of five SPLs in five objectives and the convergence speed is not significantly increased for largest LINUX SPL from 5 to 30 min. To further improve the optimization efficiency, we propose a parallel framework SMTPORT, which combines four corresponding SMTIBEA variants and performs these variants by utilizing parallelization techniques within the limited time budget. For case studies in LVAT repository, we conduct a series of experiments on seven real-world and highly-constrained SPLs. Empirical results demonstrate that our approach significantly outperforms the state-of-the-art for all the seven SPLs in terms of a quality Hypervolume metric and a diversity Pareto Front Size indicator.
Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence,...
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ISBN:
(纸本)9781450367684
Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MO-HO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 non-trivial crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.
This paper presents a flexible general-purpose framework using genetic and multi-objective evolutionary algorithms that can leverage "unlabeled" (and anonymized) panel data on television viewership along wit...
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This paper presents a flexible general-purpose framework using genetic and multi-objective evolutionary algorithms that can leverage "unlabeled" (and anonymized) panel data on television viewership along with aggregate-level vote or public opinions statistics to (i) identify sets of programs that have affinities with politics and social issues, and (ii) estimate individual preferences from unlabeled data. The applications of this framework are significant given the wide interest in using big data for political advertising and building election forecasting models with non-polling data. Analyzing viewership spanning over seven billion minutes from Nielsen's TV panel for an entire year (2016), we illustrate how this framework can learn interesting baskets of programs whose viewership can help estimate individual attitudes toward politics, global warming, same-sex marriage, and abortion.
The multi-objectiveevolutionary Algorithm based on Decomposition (MOEA/D) has shown high-performance levels when solving complicated multi-objective optimization problems. However, its adaptation for dealing with con...
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ISBN:
(纸本)9781450371285
The multi-objectiveevolutionary Algorithm based on Decomposition (MOEA/D) has shown high-performance levels when solving complicated multi-objective optimization problems. However, its adaptation for dealing with constrained multi-objective optimization problems (cMOPs) keeps being under the scope of recent investigations. This paper introduces a novel selection mechanism inspired by the epsilon-constraint method, which builds a bi-objective problem considering the scalarizing function (used into the decomposition approach of MOEA/D) and the constraint violation degree as an objective function. During the selection step of MOEA/D, the scalarizing function is considered to choose the best solutions to the cMOP. Preliminary results obtained over a set of complicated test problems drawn from the CF test suite indicate that the proposed algorithm is highly competitive regarding state-of-the-art MOEAs adopted in our comparative study.
This paper proposes a method for many-core-based large-scale parallel and distributed computation of MOEA/D, a decomposition-based evolutionarymulti-objective optimization algorithm. Standard parallel MOEA/D on many-...
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ISBN:
(纸本)9781728169293
This paper proposes a method for many-core-based large-scale parallel and distributed computation of MOEA/D, a decomposition-based evolutionarymulti-objective optimization algorithm. Standard parallel MOEA/D on many-core environments provides fast execution time, but uniformity and diversity of the Pareto front may be lost. To avoid this problem, we propose a method of defining a virtual overlapping zone between partitions and selecting individuals for mating and migration by evaluating individual populations in this area using weight vectors of adjacent partitions. Using a two-objective constrained knapsack problem for evaluation, we compare the proposed method with standard single-core execution, no-migration parallel MOEA/D, and parallel MOEA/D with standard migration, and show that the proposed method is effective in improving diversity in solution searching while shortening execution time and increasing the accuracy of solution searching.
This work proposes a parallel multi-objectiveevolutionary algorithm based on decomposition for solving constrained multi-objective optimization problems. A representative decomposition-based algorithm, MOEA/D, decomp...
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ISBN:
(纸本)9781450372114
This work proposes a parallel multi-objectiveevolutionary algorithm based on decomposition for solving constrained multi-objective optimization problems. A representative decomposition-based algorithm, MOEA/D, decomposes multi-objective problems into a number of single-objective sub-problem using weight vectors and a scalarizing function. It keeps only the best solution for each sub-problem and neighbor solutions are used to generate offspring. Therefore, to independently execute solution generation in parallel by using multi-core, at least two solutions have to be included in a core. Hence, maximum parallel number of MOEA/D-based parallel algorithm is the population size over 2. However, in proposed parallel algorithm, it can be the population size since it keeps not only the best feasible solution but also an archive population of useful infeasible solutions for each sub-problem. The experimental results using discrete knapsack problems with 2 objectives and {2, 6, 10} constraints show that the proposed parallel algorithm achieves higher search performance by utilizing infeasible solutions even if the number of parallelization is higher than a parallel decomposition-based algorithm.
Interest in network analysis has not stopped increasing over the last decade. The Community Detection Problem (CDP) has been a hot topic in network analysis, so many different approaches have been proposed. Among them...
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ISBN:
(纸本)9783030623647;9783030623654
Interest in network analysis has not stopped increasing over the last decade. The Community Detection Problem (CDP) has been a hot topic in network analysis, so many different approaches have been proposed. Among them, optimization methods have proven to be highly effective for this task. Traditionally, the CDP has been tackled as a single-objective optimization problem. Nevertheless, this trend has started to change, and new methods have appeared following multi-objective approaches. Genetic algorithms have been applied to the CDP with relative success, especially NSGA-II. However, cellular Genetic algorithms (cGAs) have yet received little attention. In cGAs, the population is structured in small overlapping neighborhoods producing a slow spread of high-quality solutions. The main contribution of this paper is understanding if the smooth diffusion scheme of MoCell (a multi-objective cGA) can provide any benefit over current multi-objective GAs for the CDP. To verify the effectiveness of MoCell, an evaluation was conducted on 21 synthetically generated networks and two real-world ones. The experiments show that MoCell is able to outperform NSGA-II, especially in large networks scenarios.
An optimization technique aims to find the best solution to an optimization problem. If the problem considers only one objective function, the best solution will provide the optimal value for such objective. However, ...
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An optimization technique aims to find the best solution to an optimization problem. If the problem considers only one objective function, the best solution will provide the optimal value for such objective. However, if the problem considers two or more objectives, the selection of solutions will not be that straightforward since such objective functions are usually in conflict. For this kind of optimization problems, the use of analytical or exact methods becomes impractical. Thus, heuristic or metaheuristic approaches have to be applied for finding the optimal solutions or, at least, approximate solutions to the optimum. As a consequence, a wide variety of metaheuristics inspired by nature has been proposed for solving optimization problems. Among them, the Grey Wolf Optimizer is a metaheuristic of recent creation that in the last few years has attracted the attention of many researchers. Furthermore, a multi-objective extension of this technique was recently introduced proving its high performance comparable to other multi-objective optimization methods. In this paper, a multi-objective grey wolf optimizer based on the decomposition is introduced. Our proposed algorithm approximates Pareto solutions cooperatively by defining a neighborhood relation among the scalarizing subproblems in which the multi-objective problem is decomposed. The performance of our proposed method is compared against those achieved by six state-of-the-art bio-inspired techniques showing its high performance in both, well-known benchmark problems and two real-life engineering problems. (C) 2018 Elsevier Ltd. All rights reserved.
Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence,...
详细信息
ISBN:
(纸本)9781450367684
Writing a test case reproducing a reported software crash is a common practice to identify the root cause of an anomaly in the software under test. However, this task is usually labor-intensive and time-taking. Hence, evolutionary intelligence approaches have been successfully applied to assist developers during debugging by generating a test case reproducing reported crashes. These approaches use a single fitness function called Crash Distance to guide the search process toward reproducing a target crash. Despite the reported achievements, these approaches do not always successfully reproduce some crashes due to a lack of test diversity (premature convergence). In this study, we introduce a new approach, called MO-HO, that addresses this issue via multi-objectivization. In particular, we introduce two new Helper-objectives for crash reproduction, namely test length (to minimize) and method sequence diversity (to maximize), in addition to Crash Distance. We assessed MO-HO using five multi-objective evolutionary algorithms (NSGA-II, SPEA2, PESA-II, MOEA/D, FEMO) on 124 non-trivial crashes stemming from open-source projects. Our results indicate that SPEA2 is the best-performing multi-objective algorithm for MO-HO. We evaluated this best-performing algorithm for MO-HO against the state-of-the-art: single-objective approach (Single-objective Search) and decomposition-based multi-objectivization approach (De-MO). Our results show that MO-HO reproduces five crashes that cannot be reproduced by the current state-of-the-art. Besides, MO-HO improves the effectiveness (+10% and +8% in reproduction ratio) and the efficiency in 34.6% and 36% of crashes (i.e., significantly lower running time) compared to Single-objective Search and De-MO, respectively. For some crashes, the improvements are very large, being up to +93.3% for reproduction ratio and -92% for the required running time.
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