Software Coupling and Cohesion Model for Measuring the Quality of Software Components

Measuring software quality requires software engineers to understand the system’s quality attributes and their measurements.The quality attribute is a qualitative property;however,the quantitative feature is needed for software measurement,which is not considered during the development of most software systems.Many research studies have investigated different approaches for measuring software quality,but with no practical approaches to quantify and measure quality attributes.This paper proposes a software quality measurement model,based on a software interconnection model,to measure the quality of software components and the overall quality of the software system.Unlike most of the existing approaches,the proposed approach can be applied at the early stages of software development,to different architectural design models,and at different levels of system decomposition.This article introduces a software measurement model that uses a heuristic normalization of the software’s internal quality attributes,i.e.,coupling and cohesion,for software quality measurement.In this model,the quality of a software component is measured based on its internal strength and the coupling it exhibits with other component(s).The proposed model has been experimented with nine software engineering teams that have agreed to participate in the experiment during the development of their different software systems.The experiments have shown that coupling reduces the internal strength of the coupled components by the amount of coupling they exhibit,which degrades their quality and the overall quality of the software system.The introduced model can help in understanding the quality of software design.In addition,it identifies the locations in software design that exhibit unnecessary couplings that degrade the quality of the software systems,which can be eliminated.

Evaluation of IoT Measurement Solutions from a Metrology Perspective

To professionally plan and manage the development and evolution of the Internet of Things(IoT),researchers have proposed several IoT performance measurement solutions.IoT performance measurement solutions can be very valuable for managing the development and evolution of IoT systems,as they provide insights into performance issues,resource optimization,predictive maintenance,security,reliability,and user experience.However,there are several issues that can impact the accuracy and reliability of IoT performance measurements,including lack of standardization,complexity of IoT systems,scalability,data privacy,and security.While previous studies proposed several IoT measurement solutions in the literature,they did not evaluate any individual one to figure out their respective measurement strengths and weaknesses.This study provides a novel scheme for the evaluation of proposed IoT measurement solutions using a metrology-coverage evaluation based on evaluation theory,metrology principles,and software measurement best practices.This evaluation approach was employed for 12 IoT measure categories and 158 IoT measurement solutions identified in a Systematic Literature Review(SLR)from 2010 to 2021.The metrology coverage of these IoT measurement solutions was analyzed from four perspectives:across IoT categories,within each study,improvement over time,and implications for IoT practitioners and researchers.The criteria in this metrology-coverage evaluation allowed for the identification of strengths and weaknesses in the theoretical and empirical definitions of the proposed IoT measurement solutions.We found that the metrological coverage varies significantly across IoT measurement solution categories and did not show improvement over the 2010–2021 timeframe.Detailed findings can help practitioners understand the limitations of the proposed measurement solutions and choose those with stronger designs.These evaluation results can also be used by researchers to improve current IoT measurement solution designs and suggest new solutions with a stronger metrology base.

Softw are Maintainability Evaluation Based on Software Configuration Management Data

As one of the most important attributes of software quality, software maintainability has been widely recognized.However,the existing maintainability evaluation methods are mostly based on subjectively judgment. Thus it is inapplicable or unbelievable. To evaluate software maintainability objectively,the software configuration management( SCM) data are collected to establish a maintainability model. Based on the hidden Markov chain( HMC), a three-state maintainability estimation model is constructed. To validate the feasibility of the model,a real software example of software maintenance activity is given and the result from the example shows the effectiveness of the proposed method.

Singularity of Some Software Reliability Models and Parameter Estimation Method

According to the principle, “The failure data is the basis of software reliability analysis”, we built a software reliability expert system (SRES) by adopting the artificial intelligence technology. By reasoning out the conclusion from the fitting results of failure data of a software project, the SRES can recommend users “the most suitable model” as a software reliability measurement model. We believe that the SRES can overcome the inconsistency in applications of software reliability models well. We report investigation results of singularity and parameter estimation methods of experimental models in SRES.

Singularity of Software Reliability Models LVLM and LVQM

According to the principle, “The failure data is the basis of software reliabilityanalysis”, we built a software reliability expert system (SRES) by adopting the artificialtechnology. By reasoning out the conclusion from the fitting results of failure data of asoftware project, the SRES can recommend users “the most suitable model” as a softwarereliability measurement model. We believe that the SRES can overcome the inconsistency inapplications of software reliability models well. We report investigation results of singularity and parameter estimation methods of models, LVLM and LVQM.

A FGR Software Model for Measuring the Effect of Technological Progress

The work of quantitative studying the effect of technological progress on economic growth, being of great complexity and far-reaching significance, has become a quite popular research topic in the world. In recent years there are a large number of scientists who are engaged in this research both at home and abroad.

Software Project Effort Estimation Based on Multiple Parametric Models Generated Through Data Clustering

Parametric software effort estimation models usually consists of only a single mathematical relationship. With the advent of software repositories containing data from heterogeneous projects, these types of models suffer from poor adjustment and predictive accuracy. One possible way to alleviate this problem is the use of a set of mathematical equations obtained through dividing of the historical project datasets according to different parameters into subdatasets called partitions. In turn, partitions are divided into clusters that serve as a tool for more accurate models. In this paper, we describe the process, tool and results of such approach through a case study using a publicly available repository, ISBSG. Results suggest the adequacy of the technique as an extension of existing single-expression models without making the estimation process much more complex that uses a single estimation model. A tool to support the process is also presented. Keywords software engineering, software measurement, effort estimation, clustering