The adoption of software measures: A technology acceptance model (TAM) perspective

The use of software measures for project management and software process improvement has been encouraged for many years. However, the low level of acceptance and use of software measures in practice has been a constant concern. In this paper we propose and test a model which explains and predicts the use of software measures. The model is based on the technology acceptance model (TAM) and operationalizes the perceived usefulness construct according to the “desirable properties of software measures.” Our research provides guidance for software engineers in selecting among different software measures and for software metrics coordinators who are planning measurement programs.     

A model of design decision making based on empirical results of interviews with software designers

Despite the impact of design decisions on software design, we have little understanding about how design decisions are made. This hinders our ability to provide design metrics, processes and training that support inherent design work. By interviewing 25 software designers and using content analysis and explanation building as our analysis technique, we provide qualitative and quantitative results that highlight aspects of rational and naturalistic decision making in software design. Our qualitative multi-case study results in a model of design decision making to answer the question: how do software designers make design decisions? We find the structure of the design problem determines the aspects of rational and naturalistic decision making used. The more structured the design decision, the less a designer considers options.     

Using multiple metrics for analysis of improvement

To make global decisions about a project or group of projects, it is necessary to analyse several metrics in concert, as well as changes in individual metrics. This paper discusses several approaches to collective metrics analysis. First, classification tree analysis is described as a technique for evaluating both process and metrics characteristics. Next, the notion of a multiple metrics graph is introduced. Developed initially as a way to evaluate software switch quality, a multiple metrics graph allows collections of metrics to be viewed in terms of overall product or process improvement.This work was done while the authors were affiliated with Contel Technology Center, Chantilly, Virginia, USA.     

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.     

基于持续集成的软件度量

软件度量是针对软件开发项目、过程及产品进行数据定义、收集和分析的持续度量化过程;持续集成工具上的的构建工程每天自动完成从版本库更新代码、静态检查、编译、出包、自动化用例测试等任务,在进行集成构建的过程中可以为软件度量提供多种相关的度量数据;结合工作实践,叙述了基于持续集成的软件度量的原理;软件度量管理涉及到的角色;软件度量实现过程;叙述了基于持续集成的两种类型的软件度量指标的定义以及提取方法;最后详细叙述了在软件度量过程中遇到的几个典型案例;工作实践表明在软件的开发过程中做好软件度量工作有助于软件开发部门控制、预测、和改进软件产品的质量与软件开发过程;从而提高软件质量和软件开发效率,降低软件开发成本。     

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.     

Misleading Metrics and Unsound Analyses

The authors demonstrate that the recommendations for analyzing productivity in the appendix to the ISO/IEC 15939 standard are inappropriate. They also show that problems with the ISO/IEC advice can be compounded if software engineers attempt to apply statistical process-control techniques to software productivity metrics. They recommend using small meaningful data sets as the basis for productivity analysis and using effort-estimation models to assess productivity rather than productivity metrics. This article is part of a special focus section of software metrics     

Software project management using PROMPT: A hybrid metrics, modeling and utility framework

In this paper, we present a ‘forward-looking’ decision support framework that integrates up-to-date metrics data with simulation models of the software development process in order to support the software project management control function. This forward-looking approach (called the PROMPT method) provides predictions of project performance and the impact of various management decisions. Tradeoffs among performance measures are accomplished using outcome based control limits (OBCLs) and are augmented using multi-criteria utility functions and financial measures of performance to evaluate various process alternatives. The decision support framework enables the program manager to plan, manage and track current software development activities in the short term and to take corrective action as necessary to bring the project back on track. The model provides insight on potential performance impacts of the proposed corrective actions. A real world example utilizing a software process simulation model is presented.     

Looking for the best software engineers

The author maintains that software managers find staffing decisions difficult because they have never been trained to be successful in the interview and selection process. He reports the results of a series of informal interviews with about 30 managers and engineers regarding the traits software managers look for in the people they have working for them and the characteristics they want to see in job candidates (external or internal) for software-engineering and technician positions on their project teams     

A new fuzzy rule based algorithm for estimating software faults in early phase of development

Estimation of reliability and the number of faults present in software in its early development phase, i.e., requirement analysis or design phase is very beneficial for developing reliable software with optimal cost. Software reliability prediction in early phase of development is highly desirable to the stake holders, software developers, managers and end users. Since, the failure data are unavailable in early phase of software development, different reliability relevant software metrics and similar project data are used to develop models for early software fault prediction. The proposed model uses the linguistic values of software metrics in fuzzy inference system to predict the total number of faults present in software in its requirement analysis phase. Considering specific target reliability, weightage of each input software metrics and size of software, an algorithm has been proposed here for developing general fuzzy rule base. For model validation of the proposed model, 20 real software project data have been used here. The linguistic values from four software metrics related to requirement analysis phase have been considered as model inputs. The performance of the proposed model has been compared with two existing early software fault prediction models.     

Controlling and predicting the quality of space shuttle software using metrics

Software quality metrics have potential for helping to assure the quality of software on large projects such as the Space Shuttle flight software. It is feasible to validate metrics for controlling and predicting software quality during design by validating metrics against a quality factor. Quality factors, like reliability, are of more interest to customers than metrics, like complexity. However quality factors cannot be collected until late in a project. Therefore the need arises to validate metrics, which developers can collect early in a project, against a quality factor. We investigate the feasibility of validating metrics for controlling and predicting quality on the Space Shuttle. The key to the approach is the use of validated metrics for early identification and resolution of quality problems.     

Table Oriented Metrics for Relational Databases

Developing and selecting high quality software applications are fundamental. It is important that the software applications can be evaluated for every relevant quality characteristic using validated metrics. Software engineers have been putting forward hundreds of quality metrics for software programs, disregarding databases. However, software data aspects are important because the size of data and their system nature contribute to many aspects of a systems quality. In this paper, we proposed some internal metrics to measure relational databases which influence its complexity. Considering the main characteristics of a relational table, we can propose the number of attributes (NA) of a table, the depth of the referential tree (DRT) of a table, and the referential degree (RD) of a table. These measures are characterized using measurement theory, particularly the formal framework proposed by Zuse. As many important issues faced by the software engineering community can only be addressed by experimentation, an experiment has been carried out in order to validate these metrics.     

OO metrics in practice

While it has long been recognized that software process improvement requires measuring both the process and its performance, experience has also shown that few universal metrics exist. The most effective measurement tools are specialized to some aspect of the task or domain being measured. The metrics as measures of code have often been related to external factors, such as software quality in the sense of defects. Software metrics studies often use single snapshots of a software project. Examining a project over a longer time frame allows consideration of other software quality facets, such as reuse and maintainability.     

Software metrics validation: Space Shuttle flight software example

Software quality metrics have potential for helping to ensure the quality of software on large projects such as theSpace Shuttle flight software. It is feasible to validate metrics for the purpose of controlling and predicting software quality during design by validating metrics against a quality factor. Quality factors, like reliability, are of more interest to customers than metrics, like complexity. However, quality factors cannot be collected until late in a project. Therefore, the need arises to validate metrics, which developers can collect early in a project, against a quality factor. We investigate the feasibility of validating metrics for controlling and predicting quality on theSpace Shuttle. The key to the approach is the use of validated metrics for early identification and resolution of quality problems.     

Fuzzy Logic Based Group Maturity Rating for Software Performance Prediction

Driven by market requirements,software services organizations have adopted various software engineering process models (such as capability maturity model (CMM),capability maturity model integration (CMMI),ISO 9001:2000,etc.) and practice of the project management concepts defined in the project management body of knowledge.While this has definitely helped organizations to bring some methods into the software development madness,there always exists a demand for comparing various groups within the organization in terms of the practice of these defined process models.Even though there exist many metrics for comparison,considering the variety of projects in terms of technology,life cycle,etc.,finding a single metric that caters to this is a difficult task.This paper proposes a model for arriving at a rating on group maturity within the organization.Considering the linguistic or imprecise and uncertain nature of software measurements,fuzzy logic approach is used for the proposed model.Without the barriers like technology or life cycle difference,the proposed model helps the organization to compare different groups within it with reasonable precision.     

A fuzzy logic based approach for phase-wise software defects prediction using software metrics

ContextThe software defect prediction during software development has recently attracted the attention of many researchers. The software defect density indicator prediction in each phase of software development life cycle (SDLC) is desirable for developing a reliable software product. Software defect prediction at the end of testing phase may not be more beneficial because the changes need to be performed in the previous phases of SDLC may require huge amount of money and effort to be spent in order to achieve target software quality. Therefore, phase-wise software defect density indicator prediction model is of great importance.ObjectiveIn this paper, a fuzzy logic based phase-wise software defect prediction model is proposed using the top most reliability relevant metrics of the each phase of the SDLC.MethodIn the proposed model, defect density indicator in requirement analysis, design, coding and testing phase is predicted using nine software metrics of these four phases. The defect density indicator metric predicted at the end of the each phase is also taken as an input to the next phase. Software metrics are assessed in linguistic terms and fuzzy inference system has been employed to develop the model.ResultsThe predictive accuracy of the proposed model is validated using twenty real software project data. Validation results are satisfactory. Measures based on the mean magnitude of relative error and balanced mean magnitude of relative error decrease significantly as the software project size increases.ConclusionIn this paper, a fuzzy logic based model is proposed for predicting software defect density indicator at each phase of the SDLC. The predicted defects of twenty different software projects are found very near to the actual defects detected during testing. The predicted defect density indicators are very helpful to analyze the defect severity in different artifacts of SDLC of a software project.     

软件测评实验室软件测试项目的度量研究

对软件测评实验室承担的软件测试项目所需的度量进行了综述、分类和研究,列举了常用的项目度量、过程度量和产品度量,给出了典型度量的使用实例。     

基于复杂网络的大型软件系统度量

随着软件系统规模的不断增大,如何度量面向对象大规模软件系统成为一个亟待解决的问题.近年来迅速发展的复杂网络理论为解决该问题提供了一个新的视角.介绍了大规模软件系统中的复杂网络现象,从元素级、模块级、网络级3个不同的粒度提出基于复杂网络的软件系统的各种度量;实现了大型Java程序复杂网络描述和度量工具JPAC.JPAC可用于分析大型Java系统的结构,并计算基于复杂网络的各度量值.     

Metrics in the software engineering curriculum

As a recognized discipline, software engineering traces its roots back to the 1968 NATO conference where the term was first used extensively to highlight the need for an engineering approach to the development of software. In the 30 years since that first “software engineering” conference, significant attempts have been made to improve the overall effectiveness of the software development process, and thus reduce the frequency and severity of software project failures. A major part of this improvement effort has been the attempt to develop quantitative measures which can be used to more accurately describe and better understand and manage the software development life cycle. Thus, many software metrics and models have been introduced during this period. In this article, we briefly trace the history of the development of software metrics and models, and then summarize the current state of the field. For discussion purposes, this entire development period is then arbitrarily divided into an Introductory Period (1971–1985), Growth Period (1985–1997) and the Current Period (1997–?). The development of metrics during each of these periods is then related to the treatment of software metrics and models in software engineering curricula during that same period. Our conclusion is that software engineering curricula have indeed reflected the state of software engineering as the work in software metrics and models has progressed. Furthermore, software engineering curricula of the future should reflect the relatively mature state that software metrics have attained, by covering the basic concepts of metrics in appropriate core courses, and more advanced metrics topics in a specialized, elective metrics course.