Guilt-based handling of software performance antipatterns in palladio architectural models

Antipatterns are conceptually similar to patterns in that they document recurring solutions to common design problems. Software performance antipatterns document common performance problems in the design as well as their solutions. The definition of performance antipatterns concerns software properties that can include static, dynamic, and deployment aspects. To make use of such knowledge, we propose an approach that helps software architects to identify and solve performance antipatterns. Our approach provides software performance feedback to architects, since it suggests the design alternatives that allow overcoming the detected performance problems.The feedback process may be quite complex since architects may have to assess several design options before achieving the architectural model that best fits the end-user expectations. In order to optimise such process we introduce a ranking methodology that identifies, among a set of detected antipatterns, the “guilty” ones, i.e. the antipatterns that more likely contribute to the violation of specific performance requirements. The introduction of our ranking process leads the system to converge towards the desired performance improvement by discarding a consistent part of design alternatives. Four case studies in different application domains have been used to assess the validity of the approach.     

Handling many-objective optimisation problems with R2 indicator and decomposition-based particle swarm optimiser

An R2 indicator-based multi-objective particle swarm optimiser (R2-MOPSO) can obtain well-convergence and well-distributed solutions while solving two and three objectives optimisation problems. However, R2-MOPSO faces difficulty to tackle many-objective optimisation problems because balancing convergence and diversity is a key issue in high-dimensional objective space. In order to address this issue, this paper proposes a novel algorithm, named R2-MaPSO, which combines the R2 indicator and decomposition-based archiving pruning strategy into particle swarm optimiser for many-objective optimisation problems. The innovations of the proposed algorithm mainly contains three crucial factors: (1) A bi-level archiving maintenance approach based on the R2 indicator and objective space decomposition strategy is designed to balance convergence and diversity. (2) The global-best leader selection is based on the R2 indicator and the personal-best leader selection is based on the Pareto dominance. Meanwhile, the objective space decomposition leader selection adopts the feedback information from the bi-level archive. (3) A new velocity updated method is modified to enhance the exploration and exploitation ability. In addition, an elitist learning strategy and a smart Gaussian learning strategy are embedded into R2-MaPSO to help the algorithm jump out of the local optimal front. The performance of the proposed algorithm is validated and compared with some algorithms on a number of unconstraint benchmark problems, i.e. DTLZ1-DTLZ4, WFG test suites from 3 to 15 objectives. Experimental results have demonstrated a better performance of the proposed algorithm compared with several multi-objective particle swarm optimisers and multi-objective evolutionary algorithms for many-objective optimisation problems.     

软件架构的非功能性需求指标和区域化支持

随着计算机科学技术的发展,越来越多的人把关注的目光投入到了计算机软件领域,而软件构架中的非功能需求凭借其在软件设计过程中的重要地位更是得到了开发人员的重视。该文以非功能需求作为研究对象进行分析,从软件架构的概念谈起,阐述了非功能需求的发展历程,并详细介绍了非功能需求的常见指标,最后提出非功能需求的区域化支持这一概念。     

Software architecture review by association

During the process of software design, software architects have their reasons to choose certain software components to address particular software requirements and constraints. However, existing software architecture review techniques often rely on the design reviewers’ knowledge and experience, and perhaps using some checklists, to identify design gaps and issues, without questioning the reasoning behind the decisions made by the architects. In this paper, we approach design reviews from a design reasoning perspective. We propose to use an association-based review procedure to identify design issues by first associating all the relevant design concerns, problems and solutions systematically; and then verifying if the causal relationships between these design elements are valid. Using this procedure, we discovered new design issues in all three industrial cases, despite their internal architecture reviews and one of the three systems being operational. With the newly found design issues, we derive eight general design reasoning failure scenarios.     

进化超多目标优化研究进展及展望

近年来,超多目标优化逐渐成为多目标优化研究的热点之一,由于超多目标优化问题具有难以寻优的高维目标空间,其研究颇有挑战性,因此受到广泛关注.现有综述性文献通常只是针对某个特定方面,缺乏系统性考察.鉴于此,首先从问题定义出发,综合考虑超多目标优化问题范畴,进行超多目标优化问题的概念辨析;其次通过对近些年的相关文献整理,系统分析超多目标优化问题进展并对其中部分经典方法加以介绍,通过对基准测试函数和性能指标的说明,围绕超多目标优化研究方法展开综合性论述;接着选取5个典型的超多目标进化算法,在2组基准测试函数和4个实际问题上分别展开仿真实验,通过性能指标和非参数检验对不同类别的算法进行理论分析;最后在明确超多目标优化研究领域的若干前沿问题的基础上,对今后的研究工作进行展望.     

基于目标部分满意度的需求设计方案评估

不同的需求设计方案对软件系统中非功能目标的实现具有不同的影响。这些非功能目标一般不能以一种绝对清晰定义的程度来满足，常常是以一种部分满意的方式实现，现实中寻求“满意”解比寻求“最优”解更符合实际情况。为了反映此特性，该文在分析目前已存在方法局限性的基础上，利用Letier和Lamsweedre提出的基于概率理论的面向目标的推理方法，对目标部分满意度建模。模型建立在客观标准基础上，在领域内具有实际物理解释，获得了不同方案对目标满意度的影响，可更好地用于指导需求分析和设计决策。     

A radial space division based evolutionary algorithm for many-objective optimization

In evolutionary many-objective optimization, diversity maintenance plays an important role in pushing the population towards the Pareto optimal front. Existing many-objective evolutionary algorithms mainly focus on convergence enhancement, but pay less attention to diversity enhancement, which may fail to obtain uniformly distributed solutions or fall into local optima. This paper proposes a radial space division based evolutionary algorithm for many-objective optimization, where the solutions in high-dimensional objective space are projected into the grid divided 2-dimensional radial space for diversity maintenance and convergence enhancement. Specifically, the diversity of the population is emphasized by selecting solutions from different grids, where an adaptive penalty based approach is proposed to select a better converged solution from the grid with multiple solutions for convergence enhancement. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems. Experimental results demonstrate the competitiveness of the proposed algorithm in terms of both convergence enhancement and diversity maintenance.     

基于帕累托前沿面曲率预估的超多目标进化算法

基于分解的超多目标进化算法是求解各类超多目标优化问题的主流方法, 其性能在很大程度上依赖于所采用参考向量与真实帕累托前沿面(Pareto front, PF)的匹配程度. 现有基于分解的超多目标进化算法尚难以同时有效处理各类PF不同的优化问题. 为此, 提出了一种基于PF曲率预估的超多目标进化算法(MaOEA-CE). 所提算法的核心包括两个方面, 首先基于对PF曲率的预估, 在每次迭代过程中生成不同的参考向量, 以渐进匹配不同类型问题的真实PF; 其次在环境选择过程中, 再基于预估的曲率选择合适的聚合函数对精英解进行挑选, 并对参考向量进行动态调整, 在维护种群多样性的同时提升种群的收敛性. 为验证MaOEA-CE的有效性, 将其与7个先进的超多目标算法在3个主流测试问题集DTLZ、WFG和MaF上进行对比, 实验结果表明MaOEA-CE具有明显的竞争力.     

Network-based distributed planning using coevolutionary agents: architecture and evaluation

A novel evolutionary planning framework (coevolutionary virtual design environment) particularly suited to distributed network-enabled design and manufacturing organizations is presented. The approach utilizes distributed evolutionary agents and mobile agents as principal object-oriented software entities that support a network-efficient evolutionary exploration of planning alternatives in which successive populations systematically select planning alternatives that reduce cost and increase throughput. This paper presents the architecture of the coevolutionary virtual design environment, and examines the network-based performance of the coevolutionary algorithms that execute in this environment. Simulation analysis examines the percentage convergence error and percentage computational advantage comparing the distributed network-based implementation to a centralized network-based implementation. The algorithms and architectures are evaluated in a realistic network setting and analyzed using models of network delays and processing times.     

一种基于分解和协同的高维多目标进化算法

现实中高维多目标优化问题普遍存在，而且其巨大的目标空间使得经典的多目标进化算法面临严峻挑战，提出一种基于分解和协同策略的高维多目标进化算法MaOEA/DCE.该算法利用混合水平正交实验设计方法产生接近于指定规模且均匀分布于聚合系数空间的权重向量，提高种群的分布性；其次，算法将差分进化算子和自适应SBX算子进行协同进化以产生高质量的子代个体，改善算法的收敛性.该算法与另外五种高性能的多目标进化算法在基准测试函数集DTLZ{1，2，4，5}上进行IGD⁺性能指标实验，结果表明MaOEA/DCE在收敛性、多样性和稳定性方面总体具有显著的性能优势.     

Visualization and comparison of architecture rationale with semantic web technologies

Deciding how to operationalize non-functional requirements (NFR) is a complex task, and several formalisms have been proposed to represent design decisions and their rationale. Unfortunately, these models can become complex (even unreadable) for designs with many alternatives and/or a well-documented rationale, which makes very hard to review and compare rationale. This paper introduces a Semantic Web-based technique to visualize and compare architecture rationale, combining Softgoal Interdependency Graphs (SIGs) with ontologies reified as named graphs. Reuse of rationale is thus facilitated by allowing architects to understand rationale of previous decisions and/or projects, though automated reuse remains unfeasible until extensive automated capture rationale happens. The approach is illustrated with a case study of Contexta, a museum integration project, using Toeska/Review, a Semantic Web-based tool.     

Many-objective evolutionary algorithm based on adaptive weighted decomposition

Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts.     

Ensemble mating selection in evolutionary many-objective search

Traditional multi-objective evolutionary algorithms have encountered difficulties when handling many-objective problems. This is due to the loss of selection pressure incurred by the growing size of objective space. A variety of environmental selection operators have been proposed to address the issue, each has its distinct benefits and drawbacks. We develop a novel ensemble framework to enhance the effectiveness and robustness of many-objective optimization. The framework incorporates multiple environmental selection operators to guide the search, which are then viewed as voters to construct a mating pool. We design an ensemble mating selection strategy that makes decisions based on the preference information provided by the voters: individuals elected by more voters will be assigned larger possibilities to enter the mating pool. By doing so, high quality offspring can be reproduced from the elected promising candidates. To accommodate the multiple selection operators for voting, the framework maintains multiple parallel populations, where each population is updated by one of the selection operators. An instantiation of the framework with three popular operators is presented as a prime example. Extensive experiments have been conducted on a number of many-objective problems to examine the effectiveness of the proposed approach. Experimental results show that the mating selection strategy is capable of improving the quality of the obtained solution set.     

进化高维多目标优化算法研究综述

首先针对常规多目标优化算法求解高维多目标优化时面临的选择压力衰减问题进行论述;然后针对该问题,按照选择机制的不同详细介绍基于Pareto支配、基于分解策略和基于性能评价指标的典型高维多目标优化算法,并分析各自的优缺点;接着立足于一种全新的性能评价指标-----R2指标,给出R2指标的具体定义,介绍基于R2指标的高维多目标优化算法,分析此类算法的本质,并按照R2指标的4个关键组成部分进行综述;最后,发掘其存在的潜在问题以及未来发展空间.     

反射式集成框架的规约描述方法研究

反射式集成框架的规约描述方法,主要研究在分布式实时应用领域基于构件的软件开发模式中集成框架的形式化规约描述问题.这种描述方法通过引入反射技术,除了描述集成框架中组成要素的业务逻辑之外,还对各要素的实时性能约束、运行时状态的变化以及可能具有的需求变更等特征进行形式化规约,从而支持软件在需求分析阶段的演化进程,并以指导与实现实时应用软件开发时业务逻辑与系统非功能性特征的关注分离.     

On the use of many quality attributes for software refactoring: a many-objective search-based software engineering approach

Search-based software engineering (SBSE) solutions are still not scalable enough to handle high-dimensional objectives space. The majority of existing work treats software engineering problems from a single or bi-objective point of view, where the main goal is to maximize or minimize one or two objectives. However, most software engineering problems are naturally complex in which many conflicting objectives need to be optimized. Software refactoring is one of these problems involving finding a compromise between several quality attributes to improve the quality of the system while preserving the behavior. To this end, we propose a novel representation of the refactoring problem as a many-objective one where every quality attribute to improve is considered as an independent objective to be optimized. In our approach based on the recent NSGA-III algorithm, the refactoring solutions are evaluated using a set of 8 distinct objectives. We evaluated this approach on one industrial project and seven open source systems. We compared our findings to: several other many-objective techniques (IBEA, MOEA/D, GrEA, and DBEA-Eps), an existing multi-objective approach a mono-objective technique and an existing refactoring technique not based on heuristic search. Statistical analysis of our experiments over 31 runs shows the efficiency of our approach.     

The socio-economics of software architecture

The paper argues that strategic decisions about software architectures need to be based on a social and economic analysis of which designs are likely to succeed and become accepted by users. Software architecture is increasingly having to take account of customisation, reuse, end-user development and system configuration. The relationship between architecture and end users’ requirements is investigated, to propose a cost-benefit framework to support reasoning about architectural choices from the perspective of end users. The relationships between architectural decisions and non-functional requirements is reviewed, and the impact on architecture is assessed using a case study of developing configurable, semi-intelligent software to support medical researchers in e-science domains.     

基于弱关联的自适应高维多目标进化算法

对现有的分解方法进行改进,提出一种基于弱关联的自适应高维多目标进化算法(WAEA).首先,提出一种基于夹角子空间的关联策略,使得一个解能与多个参考向量相关联;其次,提出弱关联概念,并基于此概念设计双模态标量函数,使算法能够更好地处理复杂PF问题,此外,算法通过检测参考向量子空间内解的数量,自适应调整惩罚参数大小,使其能有效处理各类多目标问题;最后,将WAEA算法与8种代表性的高维多目标算法进行比较,实验结果表明WAEA算法在处理复杂Pareto前沿的高维多目标问题时能更好地平衡Pareto最优解的收敛性与多样性.     

可信软件非功能需求形式化表示与可满足分析

可信软件的可信性由其功能需求和非功能需求共同来体现,其中,非功能需求的实现是可信软件获得用户对其行为实现预期目标能力的信任程度的客观依据.针对可信软件的重要性以及对可信软件的迫切需求,在可信软件的早期需求工程阶段,提出可信软件非功能需求驱动的过程策略选取方法.首先,对可信软件需求进行定义,提出由功能需求和非功能需求中的可信关注点构成可信需求,非可信关注点的非功能需求则定义为软目标,用于表达质量需求,基于模糊集合论和信息熵对可信软件非功能需求进行排序并获取可信关注点和软目标.在此基础上,提出可信软件非功能需求驱动的过程策略选取方法.传统的软件早期需求工程阶段的目标是为了获取满足需求的技术及设计决策,与此不同,本文对可信软件非功能需求进行分析的目标是获取过程策略,从过程角度解决可信软件生产问题.由于非功能需求间复杂的相关关系,尤其是因为存在冲突关系,故提出了基于可满足性问题求解方法推理过程策略的方法,选取满足可信软件非功能需求的过程策略.最后,通过第三方可信认证中心软件的案例,说明所提出方法的可行性.     

基于小生境的多目标进化算法

进化算法求解多目标优化问题平衡收敛性和多样性面临的主要挑战在两个方面：增强对帕累托最优前沿的选择压力和获得多样性良好的解集。然而,随着目标维数的增加,基于帕累托支配关系的选择标准无法有效地解决以上问题。因此,设计了一种基于小生境的多目标进化算法。基于小生境,提出了一种新的支配关系,其中,设计了一个聚合函数和一种采用目标向量角的密度估计方法分别度量候选解的收敛度和分布性。为了保证解集的收敛性,在同一个小生境内,仅仅收敛度最好的解是非支配解。为了维护解集的多样性,在任何两个不同的小生境内,一个小生境内兼具收敛度和分布性良好的解支配另一个小生境内收敛性和分布性均差的解,将提出的支配关系嵌入VaEA取代帕累托支配关系,设计了一种多目标进化算法VaEA-SDN。VaEA-SDN与NSGA-Ⅲ、VaEA、MSEA、NSGAII-CSDR、RPS-NSGAII以及CDR-MOEA等先进的算法在DTLZ(Deb-Thiele-Laumanns-Zitzler)和MaF(manyobjective function)基准测试系列问题上进行了广泛的对比仿真实验。仿真结果表明,VaEA-SDN平衡收敛收敛性...