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A risk-driven multi-objective evolutionary approach for selecting software requirements

evolutionary INTELLIGENCE 2019年第3期12卷 421-444页

作者： Amaral, Aruan Elias, Gledson Univ Fed Paraiba Informat Ctr Joao Pessoa PB Brazil

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.

关键词： Software requirements Risk management Next release problem multi-objective evolutionary algorithms Search based software engineering

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A Parallel Framework of Combining Satisfiability Modulo Theory with Indicator-Based evolutionary Algorithm for Configuring Large and Real Software Product Lines

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2019年第4期29卷 489-513页

作者： Shi, Kai Yu, Huiqun Guo, Jianmei Fan, Guisheng Chen, Liqiong Yang, Xingguang East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China Alibaba Grp Hangzhou Zhejiang Peoples R China Shanghai Inst Technol Dept Comp Sci & Informat Engn Shanghai Peoples R China

multi-objective evolutionary 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.

关键词： Constraint solving search-based software engineering software product lines multi-objective evolutionary algorithms parallelization

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Good Things Come In Threes: Improving Search-based Crash Reproduction With Helper objectives 35

Good Things Come In Threes: Improving Search-based Crash Rep...

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35th IEEE/ACM International Conference on Automated Software Engineering (ASE)

作者： Derakhshanfar, Pouria Devroey, Xavier Zaidman, Andy van Deursen, Arie Panichella, Annibale Delft Univ Technol Delft Netherlands

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.

关键词： crash reproduction search-based software testing multi-objective evolutionary algorithms

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Learning individual preferences from aggregate data: A genetic algorithm for discovering baskets of television shows with affinities to political and social interests

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114184-114184页

作者： Padmanabhan, Balaji Barfar, Arash Univ S Florida Muma Coll Business Sch Informat Syst & Management 4202 F Fowler Ave Tampa FL 33620 USA Univ Nevada Coll Business Dept Informat Syst 1664 N Virginia St Reno NV 89557 USA

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.

关键词： Genetic algorithms multi-objective evolutionary algorithms Audience analytics Aggregate data TV panel data Micro-targeting

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Constraint Handling within MOEA/D Through an Additional Scalarizing Function

Constraint Handling within MOEA/D Through an Additional Scal...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Zapotecas-Martinez, Saul Ponsich, Antonin UAM Cuajimalpa Mexico City DF Mexico UAM Azcapotzalco Mexico City DF Mexico

ISBN: (纸本)9781450371285

The multi-objective evolutionary 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.

关键词： Constrained handling techniques multi-objective evolutionary algorithms decomposition approach scalarizing functions

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Parallel and Distributed MOEA/D with Exclusively Evaluated Mating and Migration

Parallel and Distributed MOEA/D with Exclusively Evaluated M...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Sato, Yuji Sato, Mikiko Midtlyng, Mads Miyakawa, Minami Hosei Univ Fac Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Hosei Univ Grad Sch Comp & Informat Sci Tokyo Japan Sinshu Univ Dept Elect Informat Syst Engn Nagano Japan

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 evolutionary multi-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.

关键词： MOEA/D parallel and distributed processing many-core environment multi-objective evolutionary algorithms

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A Study for Parallelization of multi-objective evolutionary Algorithm Based on Decomposition and Directed Mating 2019

A Study for Parallelization of Multi-Objective Evolutionary ...

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3rd International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence (ISMSI)

作者： Miyakawa, Minami Sato, Hiroyuki Sato, Yuji Hosei Univ 3-7-2 Kajino Cho Tokyo 1848584 Japan Univ Electrocommun 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

ISBN: (纸本)9781450372114

This work proposes a parallel multi-objective evolutionary 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.

关键词： multi-objective optimization multi-objective evolutionary algorithms parallelization

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Exploring multi-objective Cellular Genetic algorithms in Community Detection Problems 21st

Exploring Multi-objective Cellular Genetic Algorithms in Com...

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21st International Conference on Intelligent Data Engineering and Automated Learning

作者： Pedemonte, Martin Panizo-LLedot, Angel Bello-Orgaz, Gema Camacho, David Univ Republica InCo Montevideo Uruguay Univ Politecn Madrid Dept Sistemas Informat Madrid Spain

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.

关键词： Static community detection problem Network analysis multi-objective evolutionary algorithms Cellular genetic algorithms

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multi-objective grey wolf optimizer based on decomposition

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 120卷 357-371页

作者： Zapotecas-Martinez, Saul Garcia-Najera, Abel Lopez-Jaimes, Antonio Univ Autonoma Metropolitana Dept Matemat Aplicadas & Sistemas Unidad Cuajimalpa Ave Vasco de Quiroga 4871 Ciudad De Mexico 05348 Mexico

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.

关键词： multi-objective evolutionary algorithms multi-objective grey wolf optimizer Decomposition-based MOEAs

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Good things come in threes: improving search-based crash reproduction with helper objectives 20

Good things come in threes: improving search-based crash rep...

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Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

作者： Pouria Derakhshanfar Xavier Devroey Andy Zaidman Arie van Deursen Annibale Panichella Delft University of Technology Delft The Netherlands

ISBN: (纸本)9781450367684

关键词： search-based software testing multi-objective evolutionary algorithms crash reproduction

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