Test effectiveness index: Integrating product metrics with process metrics

2012 ◽  
Author(s):  
Yan Zhang ◽  
Xuying Zhao ◽  
Xiaokun Zhang ◽  
Tian Zhang
Keyword(s):  
Product Metrics ◽  
Process Metrics ◽  
Effectiveness Index

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.

Analysis of Process Factors Affecting Software Quality Based on Design Review Record and Product Metrics

2016 ◽  
Vol 23 (04) ◽  
pp. 1650011 ◽  
Author(s):  
Takashi Sato ◽  
Shigeru Yamada
Keyword(s):  
Design Quality ◽  
Productivity Improvement ◽  
Process Step ◽  
Factors Affecting ◽  
Product Metrics ◽  
Standard Value ◽  
Process Metrics ◽  
Process Factors ◽  
Work Done

It is important to reliably conduct reviews and tests to assure the quality of software. In particular, reviewing design specifications raises the degree of completion of documents at each process step and leads to securing the quality of the final product. In addition, if review is done sufficiently, it would lead to productivity improvement by reducing backtrack work done to correct defects detected during development. For our quality improvement policy, the standard value of review efforts, which is one of our process metrics, has been implemented, helping the review process to be carried out reliably. However, this does not mean that the more hours spent on review, the higher the quality becomes. Therefore, we considered what factors other than review efforts assure review quality. We also considered ways to measure the product metrics of a review, obtained from the information obtained from the text of a review record, that have an influence on design quality. In a study on past reviews, there were few cases of quality engineering approaches done through human factor analysis and process evaluation and improvement approaches, but there seemed to be few cases of methods using product metrics with review records. In this paper, we analyzed the factors that influence design quality from the metrics obtained from the information of a review record.

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.

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

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

Automation of Ocean Product Metrics

2008 ◽  
Author(s):  
James Dykes ◽  
Jay Shriver ◽  
Josette Fabre ◽  
David Wang ◽  
Tom Murhpree ◽  
...  
Keyword(s):  
Product Metrics

Erythropoietin Effectiveness Index

1998 ◽  
Vol 15 (1) ◽  
pp. 57-60 ◽  
Author(s):  
George R. Bailie ◽  
Chai Luan Low ◽  
George Eisele
Keyword(s):  
Effectiveness Index

USING CLASSIFICATION TREES FOR SOFTWARE QUALITY MODELS: LESSONS LEARNED

1999 ◽  
Vol 09 (02) ◽  
pp. 217-231 ◽  
Author(s):  
TAGHI M. KHOSHGOFTAAR ◽  
EDWARD B. ALLEN ◽  
ARCHANA NAIK ◽  
WENDELL D. JONES ◽  
JOHN P. HUDEPOHL
Keyword(s):  
Software Quality ◽  
Classification Trees ◽  
Lessons Learned ◽  
Quality Models ◽  
Model Based ◽  
Software Product ◽  
High Assurance ◽  
Process Metrics ◽  
Product And Process ◽  
Software Quality Models

High software reliability is an important attribute of high-assurance systems. Software quality models yield timely predictions of quality indicators on a module-by-module basis, enabling one to focus on finding faults early in development. This paper introduces the Classification And Regression Trees (CART) a algorithm to practitioners in high-assurance systems engineering. This paper presents practical lessons learned on building classification trees for software quality modeling, including an innovative way to control the balance between misclassification rates. A case study of a very large telecommunications system used CART to build software quality models. The models predicted whether or not modules would have faults discovered by customers, based on various sets of software product and process metrics as independent variables. We found that a model based on two software product metrics had comparable accuracy to a model based on forty product and process metrics.

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