Research of Software Complexity Metrics with Security Indicator

Software protection technology has been universally emphasized, with the development of reverse engineering and static analysis techniques. So, it is important to research how to quantitatively evaluate the security of the protected software. However, there are some researchers evaluating the security of the proposed protect techniques directly by the traditional complexity metrics, which is not suffident. In order to better reflect security from software complexity, a multi-factor complexity metric based on control flow graph （CFG） is proposed, and the corresponding calculating procedures are presented in detail. Moreover, complexity density models are constructed to indicate the strength of software resisting reverse engineering and code analysis. Instance analysis shows that the proposed method is simple and practical, and can more objectively reflect software security from the perspective of the complexity.

Discussion on the Application of Complex Power Network to Software Engineering

Along with the further development of science and technology, computer hardware and the Intemet are in a rapid development, and information technology has been widely used in all fields so that complex problems are simply solved. Because of the needs for the development, software starts to mutually integrate with complex power network, making the scale of software increase greatly. Such a growing trend of software promotes soft-ware development to go beyond a general understanding and control and thus a complex system is formed. It is necessary to strengthen the research of complex network theory, and this is a new way to help people study the complexity of software systems. In this paper, the development course of complex dynamic network is introduced simply and the use of complex power network in the software engineering is summarized. Hopefully, this paper can help the crossover study of complex power network and software engineering in the future.

Auxiliary Software for Defining the Parameters of the Structural Organization of a Complex System

The developed auxiliary software serves to simplify, standardize and facilitate the software loading of the structural organization of a complex technological system, as well as its further manipulation within the process of solving the considered technological system. Its help can be especially useful in the case of a complex structural organization of a technological system with a large number of different functional elements grouped into several technological subsystems. This paper presents the results of its application for a special complex technological system related to the reference steam block for the combined production of heat and electricity.

A New Method for Measurement and Reduction of Software Complexity

This paper develops an improved structural software complexity metrics named information flow complexity which is closely related to the reliability of software. Together with the three software complexity metrics, the total software complexity is measured and some rules to reduce the complexity are presented in the paper. To illustrate and explain the process of measurement and reduction of software complexity, several examples and experiments are given. It is proposed that software complexity metrics can be measured earlier in software development and can provide substantial information of software systems whose reliabil- ity can be modeled and used in the determination of initial parameter estimation.

Software Project’s Complexity Measurement: A Case Study

Project management is a well understood management method, widely adopted today, in order to give predictable results to complex problems. However, quite often projects fail to satisfy their initial objectives. This is why studying the factors that affect the complexity of projects is quite important. In this paper, we will present the complexity factors that are related to project time, cost and quality management and then we will apply them to a number of selected projects, in order to compare the acquired results. The projects have been chosen in a way that results can be easily compared.

Mapping Software Metrics to Module Complexity: A Pattern Classification Approach

A desirable software engineering goal is the prediction of software module complexity (a qualitative concept) using automatically generated software metrics (quantitative measurements). This goal may be couched in the language of pattern classification;namely, given a set of metrics (a pattern) for a software module, predict the class (level of complexity) to which the module belongs. To find this mapping from metrics to complexity, we present a classification strategy, stochastic metric selection, to determine the subset of software metrics that yields the greatest predictive power with respect to module complexity. We demonstrate the effectiveness of this strategy by empirically evaluating it using a publicly available dataset of metrics compiled from a medical imaging system and comparing the prediction results against several classification system benchmarks.

Complexity Measure of Software Composition Framework

This article proposes a pragmatic approach to software composition based on matching criteria by mimicking integrated hardware counterpart. The tangible value of consumer goods as described by Cox instills this logical derivation of the proposed approach. As software gradually matures in component form, various software compositions can be systematically assembled from related existing software. Two application software are composed based on their functionality matching. Their composition complexities are measured by function point. The total effort unveils a noteworthy finding that high complexity software demands larger effort to deploy. Thus, the proposed software composition framework not only offers the freedom of development mandates to the development team, but also broadens the horizon of cost and project evaluations by arriving at the proper mix of constituent components in the software product.

Static Analysis and Code Complexity Metrics as Early Indicators of Software Defects

Software is an important part of automotive product development, and it is commonly known that software quality assurance consumes considerable effort in safety-critical embedded software development. Increasing the effectiveness and efficiency of this effort thus becomes more and more important. Identifying problematic code areas which are most likely to fail and therefore require most of the quality assurance attention is required. This article presents an exploratory study investigating whether the faults detected by static analysis tools combined with code complexity metrics can be used as software quality indicators and to build pre-release fault prediction models. The combination of code complexity metrics with static analysis fault density was used to predict the pre-release fault density with an accuracy of 78.3%. This combination was also used to separate high and low quality components with a classification accuracy of 79%.

Measuring the Complexity of Business Organization and Business Software Using Analytic Hierarchy Process （AHP）

The paper shows a proposition of metrics for measuring the complexity of the business organization and business software. The metrics is based on a subjective estimation of complexity of the elements from a part of the structure of business organization or business software in relation to other elements from the observed part. Estimation is performed based on the measuring scale for comparison of complexity of elements, and reaching a final conclusion on the complexity of elements in relation to other elements, through the Analytic Hierarchy Process （AHP）. Defined in this manner, the metrics represents a unique metrics for measuring the complexity of elements of business organization and business software, which enables their comparison. The paper also presents a short overview of existing metrics for measuring the complexity of business organization and business software.

Software Metric Analysis of Open-Source Business Software

Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.

Research on the Controllability of Software Networks Based on the Source-Driven Model

The complexity of software system has been increasing with software evolution, which affects the stability of software structure. Most of the existing measurement methods focus on the analysis of the macro-characteristics of the network topology, but lacked a certain depth and expansion to explore the nature of the complexity of the software structure, for this purpose, the complex network control theory was applied to the study of software network controllability. Firstly, the Source-Driver (SD) model was established based on the system control theory, the driver node sets were obtained by the minimum input theorem in the control process of software network topology;Then the relationship between the degree and center degree, the relationship between the in-degree and the out-degree of the software network topology were further analyzed owing to the non-uniqueness of the driver node sets;Finally, the values of the four indicators in the software system were compared. Experimental results show that the driver node sets in the software networks are mainly composed of nodes with low degree values, but it does not mean that the nodes whose in-degree values and out-degree values are also low;The action on control nodes and driver nodes are not random, the controllability of the driver nodes is closely related to the in-degree, when selecting the driver node sets, the network topology characteristics should be considered comprehensively, and the nodes with high degree and center degree are the first choice. The results have important guiding significance for the control, maintenance and redesign of software architecture.

Structural complexity metrics for UML class diagrams

In order to evaluate the structural complexity of class diagrams systematically and deeply, a new guiding framework of structural complexity is presented. An index system of structural complexity for class diagrams is given. This article discusses the formal description of class diagrams, and presents the method of formally structural complexity metrics for class diagrams from associations, dependencies, aggregations, generalizations and so on. An applicable example proves the feasibility of the presented method.

Cyclomatic Complexity and Lines of Code: Empirical Evidence of a Stable Linear Relationship

Researchers have often commented on the high correlation between McCabe’s Cyclomatic Complexity (CC) and lines of code (LOC). Many have believed this correlation high enough to justify adjusting CC by LOC or even substituting LOC for CC. However, from an empirical standpoint the relationship of CC to LOC is still an open one. We undertake the largest statistical study of this relationship to date. Employing modern regression techniques, we find the linearity of this relationship has been severely underestimated, so much so that CC can be said to have absolutely no explana-tory power of its own. This research presents evidence that LOC and CC have a stable practically perfect linear rela-tionship that holds across programmers, languages, code paradigms (procedural versus object-oriented), and software processes. Linear models are developed relating LOC and CC. These models are verified against over 1.2 million randomly selected source files from the SourceForge code repository. These files represent software projects from three target languages (C, C++, and Java) and a variety of programmer experience levels, software architectures, and de-velopment methodologies. The models developed are found to successfully predict roughly 90% of CC’s variance by LOC alone. This suggest not only that the linear relationship between LOC and CC is stable, but the aspects of code complexity that CC measures, such as the size of the test case space, grow linearly with source code size across lan-guages and programming paradigms.

Research on Software Production Support Structure

Firstly, it is found that process design is necessary for software process improvement after analyzing its complexity. Then, research methods and concepts framework are put forward, and the research content is also provided. The find-ings of research, including propositions of complexity of software process, the work program of complexity of software process improvement, software enterprise model and software production support structure are clarified. Finally, the demonstration, including mindbugs (cognitive barriers) in software process and the knowledge integration support structure of quality software production, is illustrated with case study. It is concluded that the research is useful for both software production and knowledge economy in the future.

Generating Mechanisms for Evolving Software Mirror Graph

Following the growing research interests in complex networks, in recent years many researchers treated static structures of software as complex networks and revealed that most of these networks demonstrate small-world effect and follow scale-free degree distribution. Different from the perspectives adopted in these works, our previous work proposed software mirror graph to model the dynamic execution processes of software and revealed software mirror graph may also be small world and scale-free. To explain how the software mirror graph evolves into a small world and scale free structure, in this paper we further proposed a mathematical model based on the mechanisms of growth, preferential attachment, and walking. This model captures some of the features of the software mirror graph, and the simulation results show that it can generate a network having similar properties to the software mirror graph. The implications are also discussed in this paper.

Building Requirements Semantics for Networked Software Interoperability

Naturally, like the web, integrated software systems in Internet will have to be distributed and heterogeneous. To im-prove the interoperability of services for SAAS, it is crucial to build requirements semantics that will cross the entire lifecycle of services especially on requirements stage. In this paper, a requirements semantics interoperability extend-ing approach called Connecting Ontologies (CO) that will act as semantics information carrier designing to facilitate the requirements identification and services composition is proposed. Semantic measurement of Chinese scenario is explored. By adopting the approach, a series of tools support for transport domain are developed and applied based on CO and DPO (Domain Problem Ontology) to enforce requirements engineering of networked software efficiently.

Cyclomatic Complexity-Based Encapsulation, Data Hiding, and Separation of Concerns

Three design principles are prominent in software development-encapsulation, data hiding, and separation of concerns. These principles are used as subjective quality criteria for both procedural and object-oriented applications. The purpose of research is to quantify encapsulation, data hiding, and separation of concerns is quantified using cyclomatic-based metrics. As a result of this research, the derived design metrics, coefficient of encapsulation, coefficient of data hiding, and coefficient of separation of concerns, are defined and applied to production software indicating whether the software has low or high encapsulation, data hiding, and separation of concerns.

CRAB—CombinatoRial Auction Body Software System

Auctions are important market mechanisms for the allocation of goods and services. Combinatorial auctions are those auctions in which buyers can place bids on combinations of items. Combinatorial auctions have many applications. The paper presents the CRAB software system. CRAB is a non-commercial software system for generating, solving, and testing of combinatorial auction problems. The system solves problems by Balas’ method or by the primal-dual algo-rithm. CRAB is implemented in Ruby and it is distributed as the file crab.rb. The system is freely available on web pag-es for all interested

分布式软件定义网络中多域流量工程的路由优化方法

针对分布式软件定义网络(software-defined networking,SDN)中流量管理调度不均衡的流量工程问题,提出一种基于负载均衡的多控制域流量路由优化的解决方案。首先分析控制消息流量的组成、域内通信及域间通信规则;然后基于4种控制消息定义控制链路流量的构成,明确链路承载流量分为控制消息流量和业务流量,建立平衡控制器负载和最小化最大链路利用率的优化模型;最后基于域内通信和域间通信提出两层路由算法。为提高模型求解精度,进一步提出改进离散萤火虫算法求解最优路由。结合ABILENE网络和GEANT网络,分析控制消息流量、控制器负载和链路负载等评价指标。实验结果表明,优化模型能有效实现控制器和链路负载均衡,控制消息流量是流量工程重要组成部分。相比集中控制模式,扁平分布式控制模式的平均控制器负载降低47.3%,最大链路利用率相差不超过15%。