Journal Article10.4018/JSSCI.2011040105
A Value-Based Framework for Software Evolutionary Testing
15
TL;DR: The proposed framework incorporates general principles in value-based software testing and makes it possible to prioritize testing decisions that are rooted in the stakeholder value propositions and allows for a cost-effective way to fulfill most valuable testing objectives first and a graceful degradation when planned testing process has to be shortened.
read more
Abstract: The fundamental objective in value-based software engineering is to integrate consistent stakeholder value propositions into the full extent of software engineering principles and practices so as to increase the value for software assets. In such a value-based setting, artifacts in software development such as requirement specifications, use cases, test cases, or defects, are not treated as equally important during the development process. Instead, they will be differentiated according to how much they are contributing, directly or indirectly, to the stakeholder value propositions. The higher the contributions, the more important the artifacts become. In turn, development activities involving more important artifacts should be given higher priorities and greater considerations in the development process. In this paper, a value-based framework is proposed for carrying out software evolutionary testing with a focus on test data generation through genetic algorithms. The proposed framework incorporates general principles in value-based software testing and makes it possible to prioritize testing decisions that are rooted in the stakeholder value propositions. It allows for a cost-effective way to fulfill most valuable testing objectives first and a graceful degradation when planned testing process has to be shortened.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
On Cognitive Computing
TL;DR: Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing.
174
Medical Image Classification Using an Optimal Feature Extraction Algorithm and a Supervised Classifier Technique
TL;DR: A new approach for automated diagnosis and classification of Magnetic Resonance MR human brain images is proposed which segregates MR brain images into normal and abnormal and employs genetic algorithm for feature selection which requires much lighter computational burden.
30
Sitting Posture Recognition and Location Estimation for Human-Aware Environment
Kenji Sugawara,Yusuke Manabe +1 more
TL;DR: This paper examines RS Cognition, which consists of many software functions for perceiving various situations like events or humans' activities in RS and develops two perceptual functions, sitting posture recognition and human's location estimation for a person, as RS perception tasks.
15
Artificial Intelligence in Software Engineering: Current Developments and Future Prospects
Farid Meziane,Sunil Vadera +1 more
- 01 Jan 2012
TL;DR: This chapter provides a survey on the use of AI for software engineering that covers the main software development phases and AI methods such as natural language processing techniques, neural networks, genetic algorithms, fuzzy logic, ant colony optimization, and planning methods.
15
On Localities of Knowledge Inconsistency
TL;DR: The author provides a formal definition of locality of inconsistency and describes how to identify clusters of inconsistent circumstances in a knowledge base, paving the way for a disciplined approach to manage knowledge inconsistency.
14
References
Search‐based software test data generation: a survey
TL;DR: Some of the work undertaken in the use of metaheuristic search techniques for the automatic generation of test data is surveyed, discussing possible new future directions of research for each of its different individual areas.
1.5K
Software unit test coverage and adequacy
TL;DR: The notion of adequacy criteria is examined together with its role in software dynamic testing and the methods for comparison and assessment of criteria are reviewed.
Software testing techniques
P. David Coward
- 01 Jan 1990
TL;DR: This chapter discusses the different aspects of software testing techniques and an important component of functional testing is an oracle, which treats the program as a box with its contents hidden from view.
1.1K
Top 10 list [software development]
Barry Boehm,Victor R. Basili +1 more
TL;DR: The article presents 10 techniques that can help reduce the flaws in your code and improve the ability of software developers to predict and control efficient software projects.
958
Evolutionary test environment for automatic structural testing
TL;DR: An evolutionary test environment has been developed that performs fully automatic test data generation for most structural test methods and the introduction of an approximation level for fitness evaluation of generated test data and the definition of an efficient test strategy for processing test goals, increases the performance of evolutionary testing considerably.
597