Quality metrics for intranet applications

As the number of intranet application increases, software developers face a new software paradigm and possibly a new set of quality requirement. The work discussed here attempts to identify practical software metrics for intranet applications. The six software quality characteristics and 32 quality sub-characteristics of the Extended ISO model are used as a basis to identify the key quality characteristics of intranet applications. From the results of a user survey, three key quality characteristics are identified; they are reliability, functionality, and efficiency. Five sub-characteristics (availability, accuracy, security, suitability and time behaviour) are found to be the key attributes of intranet applications. Finally, a set of three metrics is developed. In order to verify their validity and applicability to intranet projects, an experiment was performed by computing these metrics in five intranet applications. The metric values were then compared with the results from a user satisfaction survey. The metric values and the survey results were closely correlated; a larger score in software metrics leads to a higher user satisfaction score. It is very likely that these software metrics can effectively reflect the quality of an intranet application.     

Threshold Extraction Framework for Software Metrics

Software metrics are used to measure different attributes of software. To practically measure software attributes using these metrics, metric thresholds are needed. Many researchers attempted to identify these thresholds based on personal experiences. However, the resulted experience-based thresholds cannot be generalized due to the variability in personal experiences and the subjectivity of opinions. The goal of this paper is to propose an automated clustering framework based on the expectation maximization (EM) algorithm where clusters are generated using a simplified 3-metric set (LOC, LCOM, and CBO). Given these clusters, different threshold levels for software metrics are systematically determined such that each threshold reflects a specific level of software quality. The proposed framework comprises two major steps:the clustering step where the software quality historical dataset is decomposed into a fixed set of clusters using the EM algorithm, and the threshold extraction step where thresholds, specific to each software metric in the resulting clusters, are estimated using statistical measures such as the mean (μ) and the standard deviation (σ) of each software metric in each cluster. The paper's findings highlight the capability of EM-based clustering, using a minimum metric set, to group software quality datasets according to different quality levels.     

A practical view of software measurement and implementationexperiences within Motorola

A practical view of software measurement that formed the basis for a companywide software metrics initiative within Motorola is described. A multidimensional view of measurement is provided by identifying different dimensions (e.g., metric usefulness/utility, metric types or categories, metric audiences, etc.) that were considered in this companywide metrics implementation process. The definitions of the common set of Motorola software metrics, as well as the charts used for presenting these metrics, are included. The metrics were derived using the goal/question metric approach to measurement. A distinction is made between the use of metrics for process improvement over time across projects and the use of metrics for in-process project control. Important experiences in implementing the software metrics initiative within Motorola are also included     

面向移动Agent的软件度量工具的设计

利用软件度量工具对软件的各类质量属性度量,对于提高程序的质量有重要意义。在分析面向移动Agent的度量指标的基础上,设计并实现一种基于移动Agent的软件度量工具,该工具通过度量Agent与系统中其它Agent进行交互来获取度量所需信息并对其进行加工处理,用户可以通过度量Agent提供的接口查询度量的结果。最后给出度量指标与度量特征之间关系的实验结果。     

Bayesian network classifiers for probability-based metrics

A large number of distance metrics have been proposed to measure the difference of two instances. Among these metrics, Short and Fukunaga metric (SFM) and minimum risk metric (MRM) are two probability-based metrics which are widely used to find reasonable distance between each pair of instances with nominal attributes only. For simplicity, existing works use naive Bayesian (NB) classifiers to estimate class membership probabilities in SFM and MRM. However, it has been proved that the ability of NB classifiers to class probability estimation is poor. In order to scale up the classification performance of NB classifiers, many augmented NB classifiers are proposed. In this paper, we study the class probability estimation performance of these augmented NB classifiers and then use them to estimate the class membership probabilities in SFM and MRM. The experimental results based on a large number of University of California, Irvine (UCI) data-sets show that using these augmented NB classifiers to estimate the class membership probabilities in SFM and MRM can significantly enhance their generalisation ability.     

面向移动Agent的软件度量工具的设计

利用软件度量工具对软件的各类质量属性度量,对于提高程序的质量有重要意义.在分析面向移动Agent的度量指标的基础上,设计并实现一种基于移动Agent的软件度量工具,该工具通过度量Agent与系统中其它Agent进行交互来获取度量所需信息并对其进行加工处理,用户可以通过度量Agent提供的接口查询度量的结果.最后给出度量指标与度量特征之间关系的实验结果.     

Statische Analyse von Java-Anwendungen — Eignen sich Lines-of-Code-Metrik und Halstead-Länge?

Many of the recently developed software systems are implemented in Java. For these systems, activities presently are mainly related to software development tasks rather than to dedicated software maintenance tasks. For these Java systems, therefore, experimental confirmation of established metrics for measuring code quantities that are related to software maintenance is not available. This also includes very basic size measures such as the LOC metric and the Halstead length. In this article, the application of these metrics for Java systems as well as some of the associated difficulties are outlined. The presented results are based on experimental data and include empirical correlations between the basic size metrics as well as newly derived scaling laws which are suitable for maintenance related software measurement.     

Evaluating the change of software fault behavior with dataset attributes based on categorical correlation

Utilization of data mining in software engineering has been the subject of several research papers. Majority of subjects of those paper were in making use of historical data for decision making activities such as cost estimation and product or project attributes prediction and estimation. The ability to predict software fault modules and the ability to correlate relations between faulty modules and product attributes using statistics is the subject of this paper. Correlations and relations between the attributes and the categorical variable or the class are studied through generating a pool of records from each dataset and then select two samples every time from the dataset and compare them. The correlation between the two selected records is studied in terms of changing from faulty to non-faulty or the opposite for the module defect attribute and the value change between the two records in each evaluated attribute (e.g. equal, larger or smaller). The goal was to study if there are certain attributes that are consistently affecting changing the state of the module from faulty to none, or the opposite. Results indicated that such technique can be very useful in studying the correlations between each attribute and the defect status attribute. Another prediction algorithm is developed based on statistics of the module and the overall dataset. The algorithm gave each attribute true class and faulty class predictions. We found that dividing prediction capability for each attribute into those two (i.e. correct and faulty module prediction) facilitate understanding the impact of attribute values on the class and hence improve the overall prediction relative to previous studies and data mining algorithms. Results were evaluated and compared with other algorithms and previous studies. ROC metrics were used to evaluate the performance of the developed metrics. Results from those metrics showed that accuracy or prediction performance calculated traditionally using accurately predicted records divided by the total number of records in the dataset does not necessarily give the best indicator of a good metric or algorithm predictability. Those predictions may give wrong implication if other metrics are not considered with them. The ROC metrics were able to show some other important aspects of performance or accuracy.     

软件中间产品质量度量

本文对软件过程中的需求分析、设计阶段所产生的需求分析说明书、设计说明书等中间产品本身进行比较分析，并对现有的各种软件度量方法进行研究，提出了一套适合于中间产品的度量体系。在此基础上设计、开发一个易于应用和计算的度量系统，能比较如实地反映中间产品的特性，提取一些有用信息，从而有效地控制软件的开发，提高软件开发的效率和质量。     

Software metrics fluctuation: a property for assisting the metric selection process

ContextSoftware quality attributes are assessed by employing appropriate metrics. However, the choice of such metrics is not always obvious and is further complicated by the multitude of available metrics. To assist metrics selection, several properties have been proposed. However, although metrics are often used to assess successive software versions, there is no property that assesses their ability to capture structural changes along evolution.ObjectiveWe introduce a property, Software Metric Fluctuation (SMF), which quantifies the degree to which a metric score varies, due to changes occurring between successive system's versions. Regarding SMF, metrics can be characterized as sensitive (changes induce high variation on the metric score) or stable (changes induce low variation on the metric score).MethodSMF property has been evaluated by: (a) a case study on 20 OSS projects to assess the ability of SMF to differently characterize different metrics, and (b) a case study on 10 software engineers to assess SMF's usefulness in the metric selection process.ResultsThe results of the first case study suggest that different metrics that quantify the same quality attributes present differences in their fluctuation. We also provide evidence that an additional factor that is related to metrics’ fluctuation is the function that is used for aggregating metric from the micro to the macro level. In addition, the outcome of the second case study suggested that SMF is capable of helping practitioners in metric selection, since: (a) different practitioners have different perception of metric fluctuation, and (b) this perception is less accurate than the systematic approach that SMF offers.ConclusionsSMF is a useful metric property that can improve the accuracy of metrics selection. Based on SMF, we can differentiate metrics, based on their degree of fluctuation. Such results can provide input to researchers and practitioners in their metric selection processes.     

Client-based cohesion metrics for Java programs

One purpose of software metrics is to measure the quality of programs. The results can be for example used to predict maintenance costs or improve code quality. An emerging view is that if software metrics are going to be used to improve quality, they must help in finding code that should be refactored. Often refactoring or applying a design pattern is related to the role of the class to be refactored. In client-based metrics, a project gives the class a context. These metrics measure how a class is used by other classes in the context. We present a new client-based metric LCIC (Lack of Coherence in Clients), which analyses if the class being measured has a coherent set of roles in the program. Interfaces represent the roles of classes. If a class does not have a coherent set of roles, it should be refactored, or a new interface should be defined for the class.We have implemented a tool for measuring the metric LCIC for Java projects in the Eclipse environment. We calculated LCIC values for classes of several open source projects. We compare these results with results of other related metrics, and inspect the measured classes to find out what kind of refactorings are needed. We also analyse the relation of different design patterns and refactorings to our metric. Our experiments reveal the usefulness of client-based metrics to improve the quality of code.     

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.     

The measurement of software design quality

Software quality involves the conformance of a software product to some predefined set of functional requirements at a specified level of quality. The software is considered valid when it conforms to these “quality factors” at some acceptable level. There are a large number of quality factors against which software may be validated. This paper discusses the development of traditional software metrics in relation to the anticipated structure of a software system. The taxonomy of a software system primarily relies upon the dissection of the software system into modules. Modular design is the cornerstone of quality software, and metrics that can predict an optimum modular structure are critical. By examining the theoretical bases on quality metrics, a base set of common quantitative metrics can be devised and mapped to quality metrics in which they reside. This paper surveys existing metrics and suggests the derivation of software design metrics from software quality factors. Measurable software attributes are identified and suggested as potential design metrics.     

Definition and collection of metrics for comprehensive software measurement

The approach to defining software metrics in terms of software models such as control graphs or call graphs has two limitations. First, since a model of the software will not, by its very nature, have all the properties of the software itself, the model-based approach is not well suited as a basis for comprehensive programmes of software measurement. Secondly, although software metrics are usually well defined in terms of the software models to which they relate, the models themselves are often not well defined in terms of the source code, or other software artefact, which they purport to measure. The paper describes approaches to metric definition designed to overcome these limitations.     

Energy Metric for Software Systems

Acknowledging the intense requirement for low power operation in most portable computing systems, this paper introduces the notion of energy efficient software design and proposes metrics, for evaluating software systems in terms of their energy consumption. Considering the sources of power consumption in every digital circuit, and the fact that power is primarily dependent on the executing software, appropriate energy measures are derived, which can be extracted from the flowgraph of a program. The proposed measures are computed by applying rules common to the existing hierarchical measures of other internal software attributes, and form the basis for the definition of a software energy metric. This metric can be used in order to determine the level of energy consumption of any software system more efficiently than existing assembly-parsing techniques, with only a limited penalty in accuracy. Application to different implementations of algorithms, drawn from matrix algebra and multimedia, demonstrates the efficiency of the proposed energy metric for comparison purposes, and as an indicator for quality improvement.     

A complexity metric approach to software quality measurement

Traditional statistical quality control for dealing with quality variation during mass production is not appropriate for software quality assessment. Therefore determination and measurement of user desirable software attributes become a subject of research interest. In this paper, software attributes are classified into attributes of subjective judgment, attributes based on complexity metrics, and attributes with rigorous mathematical definition. Methods of measuring those attributes are proposed in the paper. To measure the quality of software as a whole, model that integrates qualitative software attributes and quantitative complexity metrics is also given. Finally, distribution of software life cycle costs in relation to the weighting of software quality attributes is discussed.     

Complexity measures for assembly language programs

This paper presents the results of a study of the software complexity characteristics of a large real-time signal processing system for which there is a 6-yr maintenance history. The objective of the study was to compare values generated by software metrics to the maintenance history in order to determine which software complexity metrics would be most useful for estimating maintenance effort. The metrics that were analyzed were program size measures, software science measures, and control flow measures. During the course of the study two new software metrics were defined. The new metrics, maximum knot depth and knots per jump ratio, are both extensions of the knot count metric. When comparing the metrics to the maintenance data the control flow measures showed the strongest positive correlation.     

A case study of tracking software development using quality metrics

This paper presents a case history of Mentor Graphics using a set of quality metrics to track development progress for a recent major software release. It provides background on how Mentor Graphics originally began using software metrics to measure product quality, how this became accepted, and how these metrics later fell out of favour. To restore these metrics to effective use, process changes were required for setting quality and metric targets, and for the way the metrics are used for tracking development progress. With these process changes in place, and the addition of a new metric, the case history demonstrates that the metric set could be used effectively to indicate problems in this release and help manage changes to the plan for completion of the release. The lessons learned in this case history are presented, along with subsequent data that further validates these metrics.     

Two-dimensional metric tensor visualization using pseudo-meshes

Riemannian metric tensors are used to control the adaptation of meshes for finite element and finite volume computations. To study the numerous metric construction and manipulation techniques, a new method has been developed to visualize two-dimensional metrics without interference from an adaptation algorithm. This method traces a network of orthogonal tensor lines, tangent to the eigenvectors of the metric field, to form a pseudo-mesh visually close to a perfectly adapted mesh but without many of its constraints. Anisotropic metrics can be visualized directly using such pseudo-meshes but, for isotropic metrics, the eigensystem is degenerate and an anisotropic perturbation has to be used. This perturbation merely preserves directional information usually present during metric construction and is small enough, about 1% of the prescribed target element size, to be visually imperceptible. Both analytical and solution-based examples show the effectiveness and usefulness of the present method. As an example, pseudo-meshes are used to visualize the effect on metrics of Laplacian-like smoothing and gradation control techniques. Application to adaptive quadrilateral mesh generation is also discussed.