MaDHS: Many‐objective discrete harmony search to improve existing package design

Recently, many computational intelligence algorithms have been proposed to address software remodularization problem. Unfortunately, it has been observed that the performance of optimizers degrades with the optimization problem containing more than three objectives. In this paper, we propose a many‐objective discrete harmony search (MaDHS) to address the software remodularization problem having more than three objectives. The basic idea of MaDHS is that it uses the quality indicator I_? + and external archive to rank and store the nondominated solutions. Along with MaDHS, five remodularization objectives, ie, low coupling, high cohesion, low modification degree, quality of class distribution, and low package instability have also been adapted to improve the package structure of existing object‐oriented software systems. To improve the accuracy of modularization solution, the coupling and cohesion objectives are formulated in terms of various dimensions of direct coupling relationships. To test the supremacy of the proposed approach, it is evaluated over eight real‐world object‐oriented software systems. Simulation results show that the proposed approach outperforms the other existing approaches in terms of couplings, cohesion, modularization quality, modularization merit factor, rate per refactoring of achieved improvement, and external developers view.     

完善验证的凝聚力测量方法以提升软件品质

无适当使用软件测量将可能引起软件低品质且高成本的窘态.凝聚力是软件品质重要因子之一如同维护度,可靠度和再利用度.软件模组品质的优劣必影响整体系统之品质的优劣.为了设计和维护高品质软件,软件专案经理人和软件工程师无可避免需引用软件凝聚力测量以衡量和产生高品质软件.提出以活路跃变量及视觉化变量纵距为分析基础之功能导向凝聚力测量方法.进而,以一系列实际案例来作实验验证,并以一组性质来作理论辩证所提的测量方法.因此一经完善定义,完善实验和完善辩证之凝聚力测量方法被提出用于当软件凝聚力强度的指标和因此增进软件品质.这凝聚力测量方法能容易嵌入CASE以帮助软件工程师确保软件品质.     

面向代码设计质量监控的软件度量指标集研究

随着软件的演化,软件规模和复杂性的上升往往会造成代码设计质量的退化,进而导致代码可维护性下降。已有大量软件度量指标用于量化代码设计质量,但是由于数量众多,不同指标体系度量结果可比性较差,使得开发人员难以找到存在设计质量问题的模块。系统调研并整理归类了现有的规模、耦合、内聚、封装、继承和多态等6方面软件度量指标。结合度量指标关系以及实验分析,从每个方面的指标集中发现了与代码设计质量关联度最高的6个指标,从而提出一种面向代码设计质量监控的软件度量指标集。实验表明,该指标集可以有效挑选出存在代码设计质量问题的类,可作为开发人员监控和定位代码设计问题的重点关注指标。     

Search-Based Cost-Effective Software Remodularization

Software modularization is a technique used to divide a software system into independent modules (packages) that are expected to be cohesive and loosely coupled. However, as software systems evolve over time to meet new requirements, their modularizations become complex and gradually loose their quality. Thus, it is challenging to automatically optimize the classes’ distribution in packages, also known as remodularization. To alleviate this issue, we introduce a new approach to optimize software modularization by moving classes to more suitable packages. In addition to improving design quality and preserving semantic coherence, our approach takes into consideration the refactoring effort as an objective in itself while optimizing software modularization. We adapt the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) of Deb et al. to find the best sequence of refactorings that 1) maximize structural quality, 2) maximize semantic cohesiveness of packages (evaluated by a semantic measure based on WordNet), and 3) minimize the refactoring effort. We report the results of an evaluation of our approach using open-source projects, and we show that our proposal is able to produce a coherent and useful sequence of recommended refactorings both in terms of quality metrics and from the developer’s points of view.     

Modularity analysis of use case implementations

A component-based decomposition can result in implementations having use cases code tangled with other concerns and scattered across components. Modularity mechanisms such as aspects, mixins, and virtual classes have been proposed to address this kind of problem. One can use such mechanisms to group together code related to a single use case. This paper quantitatively analyzes the impact of this kind of use case modularization. We apply one specific technique, aspect oriented programming, to modularize the use case implementations of two information systems that conform to the layered architecture pattern. We extract traditional and contemporary metrics – including cohesion, coupling, and separation of concerns – to analyze modularity in terms of quality attributes such as changeability, support for independent development, and pluggability. Our findings indicate that the results of a given modularity analysis depend on other factors beyond the chosen system, metrics, and the applied modularity technique.     

Learning from optimization: A case study with Apache Ant

ContextSoftware architecture degrades when changes violating the design-time architectural intents are imposed on the software throughout its life cycle. Such phenomenon is called architecture erosion. When changes are not controlled, erosion makes maintenance harder and negatively affects software evolution.ObjectiveTo study the effects of architecture erosion on a large software project and determine whether search-based module clustering might reduce the conceptual distance between the current architecture and the design-time one.MethodTo run an exploratory study with Apache Ant. First, we characterize Ant’s evolution in terms of size, change dispersion, cohesion, and coupling metrics, highlighting the potential introduction of architecture and code-level problems that might affect the cost of changing the system. Then, we reorganize the distribution of Ant’s classes using a heuristic search approach, intending to re-emerge its design-time architecture.ResultsIn characterizing the system, we observed that its original, simple design was lost due to maintenance and the addition of new features. In optimizing its architecture, we found that current models used to drive search-based software module clustering produce complex designs, which maximize the characteristics driving optimization while producing class distributions that would hardly be acceptable to developers maintaining Ant.ConclusionThe structural perspective promoted by the coupling and cohesion metrics precludes observing the adequate software module clustering from the perspective of software engineers when considering a large open source system. Our analysis adds evidence to the criticism of the dogma of driving design towards high cohesion and low coupling, at the same time observing the need for better models to drive design decisions. Apart from that, we see SBSE as a learning tool, allowing researchers to test Software Engineering models in extreme situations that would not be easily found in software projects.     

面向对象软件内聚度度量数据分布的实证研究

度量数据的分布信息对于理解和使用面向对象软件度量有重要意义.人们对面向对象软件规模度量、耦合度度量乃至继承维度的度量数据的分布都有研究,但对除内聚度缺乏度LCOM之外的内聚度度量数据的分布却缺乏研究.已有的实证研究表明,LCOM并不是好的内聚度度量,因此探讨其他内聚度度量数据分布很有必要.对包括内聚度缺乏度、基于连通性的内聚度度量和基于相似性的内聚度度量总共17个度量指标在112个Java开源软件项目的分布情况进行实证研究,对每个度量指标的每个项目数据使用幂律分布和对数正态分布进行拟合,并使用荟萃分析方法对拟合结果进行了分析.实证研究结果表明,非规范化的内聚度量可使用对数正态分布和幂律分布拟合,但规范化的基于相似性的内聚度量（包括CC、LSCC、SCOM和SCC）需要排除方法数小于等于1或字段数为0的特殊类才能使用对数正态分布拟合,而基于连通性的内聚度度量（包括TCC、LCC、DCD和DCI）则只有对应的非规范化版本的数据才符合对数正态分布或幂律分布.实证研究可帮助人们更好地理解和使用内聚度度量,特别是可以帮助人们如何利用已有的方法确定内聚度度量的阈值.     

Empirical analysis of CK metrics for object-oriented design complexity: implications for software defects

To produce high quality object-oriented (OO) applications, a strong emphasis on design aspects, especially during the early phases of software development, is necessary. Design metrics play an important role in helping developers understand design aspects of software and, hence, improve software quality and developer productivity. In this paper, we provide empirical evidence supporting the role of OO design complexity metrics, specifically a subset of the Chidamber and Kemerer (1991, 1994) suite (CK metrics), in determining software defects. Our results, based on industry data from software developed in two popular programming languages used in OO development, indicate that, even after controlling for the size of the software, these metrics are significantly associated with defects. In addition, we find that the effects of these metrics on defects vary across the samples from two programming languages-C++ and Java. We believe that these results have significant implications for designing high-quality software products using the OO approach.     

代码质量静态度量的研究与应用

代码质量度量是软件质量分析的一个重要研究方向。静态分析方法因其具有成本低、容易实现而且不依赖于程序特定的运行环境的优点,在当前软件网络化、服务化的趋势下倍受关注。针对Java代码质量度量进行研究,使用Ant工具整合各种开源的静态测试工具,并制定基于静态分析的Java代码质量综合评价方案,可支持包括代码规模、规范性、可维护性、可扩展性和潜在危险等方面的综合检测,为项目的开发者、管理者和使用者提供了实用的代码质量评价方法。     

Cohesion and coupling metrics for ontology modules

In recent years, an increasing number of ontologies and semantic web applications have been developed and used. A conscious effort has been made to develop methods to modularize ontologies. These methods contribute to building a new ontology. However, few studies have focused on the evaluative methods for ontology modules. In this study, we propose novel metrics to measure ontology modularity. To evaluate the ontology modules, we introduce cohesion and coupling based on the theory of software metrics. A cohesion metric and two coupling metrics were used to measure cohesion and coupling for ontology modules. The proposed metrics provide more detailed support in considering the different types of relationships between classes in ontology modules. In addition, the new coupling metrics contribute to checking the consistency between the ontology modules and their original ontology. The proposed metrics were validated using well-known verification frameworks and empirical experiments to complement the previous investigations. The results of this study offer ontology engineers valuable criteria with which to evaluate ontology modules and help ontology users select qualifying ontology modules.     

抑制孤立簇的软件模块化优化算法

针对传统软件模块化指标在解决软件模块化问题时容易导致孤立簇的问题,提出改进型软件模块化指标IMQ作为进化算法的适应函数以有效抑制孤立簇现象,并以IMQ最大化为目标建立软件模块化的数学规划模型,设计符合问题特点的基于相似度竞争和选择机制的改进遗传算法（IGA）求解该模型。首先,运用边收缩方法的启发式策略生成高质量的初始解,并将其作为种子植入到初始种群中;然后,利用IGA对模型进行求解,在提升搜索效率的同时进一步提高解的质量;最后,运用真实数据和仿真数据进行对比实验。实验结果表明IMQ指标能有效减少孤立簇的数目,而IGA比传统的多点爬山算法（IHC）和基于分组编码（GNE）的遗传算法具有更强的寻优能力和鲁棒性。     

面向对象软件中类内聚度度量分析与研究*

对现有著名的类内聚度度量进行了较深入的分析,指出了各自存在的缺陷与不足,并在此基础上考虑到类内聚度受到类内有可能与外界组件产生耦合的成员的负面影响,提出了对CCM(C)再次修正后的MCCM2(C).     

Efficiency of CAMEtools in software quality assurance

Our paper describes the requirements and possibilities of integration of metrics tools in the field of software quality assurance. Tools for the support of the measurement process are herein classified as Computer Assisted Software Measurement and Evaulation Tools (CAMETools). Software measurement regarded as a special type of metrics application provides a great amount of basic information for the evaluation of the software development process or the software product itself. Our paper examines the effectiveness and destination of software measurement in tool-based software development and is based on an analysis of more than 20 CAME tools in the Software Measurement Laboratory at the University of Magdeburg. CAMEtools are useable for the process, product, and resources evaluation in all phases of the software life cycle (including the problem definition) for different development paradigms. The efficiency of CAME tools is described on the basis of a general measurement framework. This framework includes all steps in the software measurement and evaulation process: metrics definition, selection of the evaluation criteria, tool-based modelling and measurement, value presentation and statistical analysis. The framework includes the main aspects of the process evaluation techniques (Capability Maturity Model, ISO 9000-3 etc.) and product evaluation (ISO 9126, etc.). It is not a disjointed set of aspects: our measurement framework represents an incremental technique for the application of quantification of quality aspects in a required quality assurance     

Identification of Move Method Refactoring Opportunities

Placement of attributes/methods within classes in an object-oriented system is usually guided by conceptual criteria and aided by appropriate metrics. Moving state and behavior between classes can help reduce coupling and increase cohesion, but it is nontrivial to identify where such refactorings should be applied. In this paper, we propose a methodology for the identification of Move Method refactoring opportunities that constitute a way for solving many common Feature Envy bad smells. An algorithm that employs the notion of distance between system entities (attributes/methods) and classes extracts a list of behavior-preserving refactorings based on the examination of a set of preconditions. In practice, a software system may exhibit such problems in many different places. Therefore, our approach measures the effect of all refactoring suggestions based on a novel Entity Placement metric that quantifies how well entities have been placed in system classes. The proposed methodology can be regarded as a semi-automatic approach since the designer will eventually decide whether a suggested refactoring should be applied or not based on conceptual or other design quality criteria. The evaluation of the proposed approach has been performed considering qualitative, metric, conceptual, and efficiency aspects of the suggested refactorings in a number of open-source projects.     

A Model-Based Approach to Object-Oriented Software Metrics

The need to improve software productivity and software quality has put forward the research on software metrics technology and the development of software metrics tool to support related activities.To support object-oriented software metrics practice efectively,a model-absed approach to object-oriented software metrics is proposed in this paper.This approach guides the metrics users to adopt the quality metrics model to measure the object-oriented software products .The development of the model can be achieved by using a top-down approach.This approach explicitly proposes the conception of absolute normalization computation and relative normalization computation for a metrics model.Moreover,a generic software metrics tool-Jade Bird Object-Oriented Metrics Tool(JBOOMT)is designed to implement this approach.The parser-based approach adopted by the tool makes the information of the source program accurate and complete for measurement.It supports various customizable hierarchical metrics models and provides a flexible user interface for users to manipulate the models.It also supports absolute and relative normalization mechanisms in different situations.     

Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems

High cohesion is a desirable property of software as it positively impacts understanding, reuse, and maintenance. Currently proposed measures for cohesion in Object-Oriented (OO) software reflect particular interpretations of cohesion and capture different aspects of it. Existing approaches are largely based on using the structural information from the source code, such as attribute references, in methods to measure cohesion. This paper proposes a new measure for the cohesion of classes in OO software systems based on the analysis of the unstructured information embedded in the source code, such as comments and identifiers. The measure, named the Conceptual Cohesion of Classes (C3), is inspired by the mechanisms used to measure textual coherence in cognitive psychology and computational linguistics. This paper presents the principles and the technology that stand behind the C3 measure. A large case study on three open source software systems is presented which compares the new measure with an extensive set of existing metrics and uses them to construct models that predict software faults. The case study shows that the novel measure captures different aspects of class cohesion compared to any of the existing cohesion measures. In addition, combining C3 with existing structural cohesion metrics proves to be a better predictor of faulty classes when compared to different combinations of structural cohesion metrics.     

A task allocation optimizer for software construction

Task allocation during the construction stage of software engineering is complex and challenging. First, engineers must chart a path between the often conflicting objectives of time and quality. Second, a huge productivity variance exists across the spectrum of practicing software developers. Properly handling this variance amid those time and quality pressures is a tricky management problem. Multiobjective optimization might provide the answer. This emerging research area generates optimal solutions for projects with many objectives. An experienced decision-maker analyzes these solutions and selects the best one. Here, we describe such an approach and demonstrate it with a problem involving the allocation of software construction tasks among a team of software developers with varying degrees of skill.     

专家系统规则库覆盖度量的研究及实践

针对专家系统规则库的软件测试和评价是保证专家系统质量与可靠性的重要环节。基于数据流的结构化测试技术,提出了一种规则库执行流图RBEF,用于描述规则库中不同规则之间相互连结、制约关系,进而给出了一组规则库覆盖度量准则和测试用例的设计方法,最后讨论了一个已实现并付诸于应用的基于RBEF的软件测试工具ESRTS。     

Software defect prediction using Bayesian networks

There are lots of different software metrics discovered and used for defect prediction in the literature. Instead of dealing with so many metrics, it would be practical and easy if we could determine the set of metrics that are most important and focus on them more to predict defectiveness. We use Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness. In addition to the metrics used in Promise data repository, we define two more metrics, i.e. NOD for the number of developers and LOCQ for the source code quality. We extract these metrics by inspecting the source code repositories of the selected Promise data repository data sets. At the end of our modeling, we learn the marginal defect proneness probability of the whole software system, the set of most effective metrics, and the influential relationships among metrics and defectiveness. Our experiments on nine open source Promise data repository data sets show that response for class (RFC), lines of code (LOC), and lack of coding quality (LOCQ) are the most effective metrics whereas coupling between objects (CBO), weighted method per class (WMC), and lack of cohesion of methods (LCOM) are less effective metrics on defect proneness. Furthermore, number of children (NOC) and depth of inheritance tree (DIT) have very limited effect and are untrustworthy. On the other hand, based on the experiments on Poi, Tomcat, and Xalan data sets, we observe that there is a positive correlation between the number of developers (NOD) and the level of defectiveness. However, further investigation involving a greater number of projects is needed to confirm our findings.     

A Component Selection Framework of Cohesion and Coupling Metrics

Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development. Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity. The complexity measurement of cohesion and coupling component to analyze the relationship between the component module. In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository. It measures the interface density modules of coupling and cohesion in a modular software system. This cohesion measurement has been taken into two parameters for analyzing the result of complexity, with the help of low cohesion and high cohesion. In coupling measures between the component of inside parameters and outside parameters. The final process of coupling and cohesion, the measured values were used for the average calculation of components parameter. This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository. The better result is observed for high cohesion and low coupling in component-based software engineering.