Software Metrics and Measurements

2011 ◽  
pp. 1029-1034 ◽  
Author(s):  
Michalis Xenos
Keyword(s):  
Decision Making ◽  
Software Development ◽  
Product Quality ◽  
Software Metrics ◽  
Testing Time ◽  
Effort Estimation ◽  
Product Metrics ◽  
Process Characteristics ◽  
Push Forward ◽  
Process Metrics

In the past few years, a large number of e-government and e-commerce systems have been developed, thus resulting to a constantly increasing number of software developers involved in software development for such systems. To ensure the production of high quality e-government and e-commerce systems, it is important for developers to collect and analyze measurable data that guide estimation, decision making, and assessment. It is common sense that one can control and manage better what he is able to measure. Although there are major differences between e-commerce and e-government (e.g., access, structure and accountability; Jorgenson & Cable, 2002) there are no significant differences in terms of software metrics that can be applied to both. Metrics are used in e-government and e-commerce software development to measure various factors related to software quality and can be classified as product metrics, process metrics and recourse metrics. Product metrics are also called software metrics. These are metrics that are directly related to the product itself, such as code statements, delivered executables, manuals, and strive to measure product quality, or attributes of the product that can be related to product quality. Process metrics focus on the process of software development and measure process characteristics, aiming to detect problems or to push forward successful practices. Resource metrics are related to the resources required for software development and their performance. This article focuses on product metrics and on how such metrics can aid in design, prediction and assessment of the final product quality, provide data used for decision making, cost and effort estimation, fault prevention, testing time reduction, and, consequently, aid in producing better software for e-government and e-commerce sys

Technical Issues Related to IT Governance Tactics

2011 ◽  
pp. 216-245
Author(s):  
Michalis Xenos
Keyword(s):  
Decision Making ◽  
Process Control ◽  
Development Process ◽  
Measurement Techniques ◽  
It Governance ◽  
Testing Time ◽  
Effort Estimation ◽  
Statistical Process ◽  
Product Metrics ◽  
Control Techniques

This chapter deals with some technical aspects of the strategies for IT Governance described in most of the other chapters of this book. The practical application of IT Governance techniques and tactics requires the collection and analysis of measurable data that guide estimation, decision-making and assessment, since it is common sense that one can control and manage better what one is able to measure. This chapter aims at introducing the reader to product metrics and measurements, proposing methods relating to the implementation of a measurements program and analyzing how product metrics can be used to control IT development. Focus is placed on product metrics, measurements and process control techniques. Such instruments can aid significantly in monitoring the development process and making IT-related tasks more transparent to IT managers. They also aid in design, prediction and assessment of the IT product quality. They provide data used for decision-making, cost and effort estimation, fault prevention, and testing time reduction. Moreover, the use of product metrics and process control techniques can direct the standardization of IT products and IT development process, as well as the assessment of the process maturity of outsourcing partners. The most commonly used metrics are presented and the reader is introduced to measurement techniques and automation tools. This chapter also discusses the application of metrics along with statistical process control tools. Finally, it provides suggestions on how product metrics can aid towards IT Governance not only in large IT companies, but also in the smaller ones.

Exploring the Effects of Process Characteristics on Products Quality in Open Source Software Development

2009 ◽  
pp. 3008-3036 ◽  
Author(s):  
Stefan Koch ◽  
Christian Neumann
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Open Source Software Development ◽  
Development Environment ◽  
Product Metrics ◽  
Software Products ◽  
Process Characteristics ◽  
Development Processes ◽  
Process Metrics

There has been considerable discussion on the possible impacts of open source software development practices, especially in regard to the quality of the resulting software product. Recent studies have shown that analyzing data from source code repositories is an efficient way to gather information about project characteristics and programmers, showing that OSS projects are very heterogeneous in their team structures and software processes. However, one problem is that the resulting process metrics measuring attributes of the development process and of the development environment do not give any hints about the quality, complexity, or structure of the resulting software. Therefore, we expanded the analysis by calculating several product metrics, most of them specifically tailored to object-oriented software. We then analyzed the relationship between these product metrics and process metrics derived from a CVS repository. The aim was to establish whether different variants of open source development processes have a significant impact on the resulting software products. In particular we analyzed the impact on quality and design associated with the numbers of contributors and the amount of their work, using the GINI coefficient as a measure of inequality within the developer group.

Exploring the Effects of Process Characteristics on Product Quality in Open Source Software Development

2011 ◽  
pp. 132-159 ◽  
Author(s):  
Stefan Koch ◽  
Christian Neumann
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Open Source Software Development ◽  
Development Environment ◽  
Product Metrics ◽  
Software Products ◽  
Process Characteristics ◽  
Development Processes ◽  
Process Metrics

There has been considerable discussion on the possible impacts of open source software development practices, especially in regard to the quality of the resulting software product. Recent studies have shown that analyzing data from source code repositories is an efficient way to gather information about project characteristics and programmers, showing that OSS projects are very heterogeneous in their team structures and software processes. However, one problem is that the resulting process metrics measuring attributes of the development process and of the development environment do not give any hints about the quality, complexity, or structure of the resulting software. Therefore, we expanded the analysis by calculating several product metrics, most of them specifically tailored to object-oriented software. We then analyzed the relationship between these product metrics and process metrics derived from a CVS repository. The aim was to establish whether different variants of open source development processes have a significant impact on the resulting software products. In particular we analyzed the impact on quality and design associated with the numbers of contributors and the amount of their work, using the GINI coefficient as a measure of inequality within the developer group.

Software Metrics and Measurements

2009 ◽  
pp. 172-179
Author(s):  
Michalis Xenos
Keyword(s):  
Decision Making ◽  
Software Development ◽  
Common Sense ◽  
Software Metrics ◽  
High Quality ◽  
Software Developers ◽  
The Past

In the past few years, a large number of e-government and e-commerce systems have been developed, thus resulting to a constantly increasing number of software developers involved in software development for such systems. To ensure the production of high quality e-government and ecommerce systems, it is important for developers to collect and analyze measurable data that guide estimation, decision making, and assessment. It is common sense that one can control and manage better what he is able to measure.

Implementing Software Metrics at a Telecommunications Company

2011 ◽  
pp. 58-76
Author(s):  
David I. Heimann
Keyword(s):  
Software Development ◽  
Organizational Structure ◽  
Product Quality ◽  
Software Metrics ◽  
High Quality ◽  
Telecommunications Company

Establishing and using fundamental measures of software development progress is an essential part of being able to predictably produce high-quality customer-satisfying software within a reasonable agreed-to timeframe. However, in many organizations such measurement is done incompletely or not at all. While various forces encourage measurement programs, others form barriers to such implementation. This case study explores a formal Metrics Program established to track and analyze the development of a new version for the major voicemail product of a mid-sized telecommunications company. The study addresses the evolution of the company’s organizational structure and culture that led to the adoption of the program, the components and structure of the program, the implementation of the program, its effects on product quality and timeliness, and what happened thereafter. The study also raises questions with respect to facilitating an organizational atmosphere where a Metrics Program can flourish.

An Empirical Study on Software Fault Prediction Using Product and Process Metrics

2021 ◽  
Vol 14 (1) ◽  
pp. 62-78
Author(s):  
Raed Shatnawi ◽  
Alok Mishra
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Product Metrics ◽  
Software Models ◽  
Software Fault ◽  
Process Metrics ◽  
Product And Process ◽  
Fault Proneness

Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-proneness of modules (classes and interfaces). Classifiers are trained and tested for five large open-source systems. Classifiers were built using product metrics alone and using a combination of product and the proposed process metric. The classifiers evaluation shows improvements whenever the process metrics were used. Evolution metrics are correlated with quality of software and helps in improving software quality prediction for future releases.

PENGARUH KUALITAS PRODUK DAN PROMOSI TERHADAP KEPUTUSAN PEMBELIAN BUAH MELON PT. SYAFINA NIAGA

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Iwan Kurniawan Subagja, SE., MM. ◽  
Slamet Istoto
Keyword(s):  
Decision Making ◽  
Product Quality ◽  
Sampling Methods ◽  
Marketing Mix ◽  
Sampling Techniques ◽  
Purchasing Decisions ◽  
Individual Activity ◽  
A Company ◽  
Melon Fruit

Decision making is an individual activity that is directly involved in obtaining and using the goods offered. Promotion is one of the variables in a marketing mix that is very important to be implemented by a company in marketing its products or services, thus influencing consumers to make purchasing decisions. The quality of the product can also affect consumers in making purchasing decisions. This study aims to analyze the effect of product quality and promotion on purchasing decision of Melon fruit. Population and sample of research is purchasing or buyer from consumer of PT. Syafina Niaga as many as 30 companies and sampling methods using saturated sampling techniques as well as analysis used with regression analysis. The results showed that there is a significant influence between product quality and promotion of purchasing decisions.

Computable metrics of personal performance in software development teams

2019 ◽  
Author(s):  
Andriy Lishchytovych ◽  
Volodymyr Pavlenko
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Product Quality ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Project Manager ◽  
Development Life Cycle ◽  
Software Product ◽  
Software Development Life Cycle

The present article describes setup, configuration and usage of the key performance indicators (KPIs) of members of project teams involved into the software development life cycle. Key performance indicators are described for the full software development life cycle and imply the deep integration with both task tracking systems and project code management systems, as well as a software product quality testing system. To illustrate, we used the extremely popular products - Atlassian Jira (tracking development tasks and bugs tracking system) and git (code management system). The calculation of key performance indicators is given for a team of three developers, two testing engineers responsible for product quality, one designer, one system administrator, one product manager (responsible for setting business requirements) and one project manager. For the key members of the team, it is suggested to use one integral key performance indicator per the role / team member, which reflects the quality of the fulfillment of the corresponding role of the tasks. The model of performance indicators is inverse positive - the initial value of each of the indicators is zero and increases in the case of certain deviations from the standard performance of official duties inherent in a particular role. The calculation of the proposed key performance indicators can be fully automated (in particular, using Atlassian Jira and Atlassian Bitbucket (git) or any other systems, like Redmine, GitLab or TestLink), which eliminates the human factor and, after the automation, does not require any additional effort to calculate. Using such a tool as the key performance indicators allows project managers to completely eliminate bias, reduce the emotional component and provide objective data for the project manager. The described key performance indicators can be used to reduce the time required to resolve conflicts in the team, increase productivity and improve the quality of the software product.

scholarly journals Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1195
Author(s):  
Priya Varshini A G ◽  
Anitha Kumari K ◽  
Vijayakumar Varadarajan
Keyword(s):  
Deep Learning ◽  
Random Forest ◽  
Software Development ◽  
Weighted Averaging ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Single Model ◽  
Ensemble Techniques ◽  
Project Estimation

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

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