Application of machine learning techniques to the flexible assessment and improvement of requirements quality

Moreno, Valentín; Génova, Gonzalo; Parra, Eugenio; Fraga, Anabel

doi:10.1007/s11219-020-09511-4

Cited by 7 publications

(7 citation statements)

References 55 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Subjective methods involve human judgment and expert opinions, such as manual reviews [21,22], walkthroughs [23,24], and surveys [24,25]. Objective methods, on the other hand, employ automated tools and formal techniques, such as static analysis [26], natural language processing [27][28][29][30], machine learning [20,31], and deep learning [32]. Objective methods have gained increasing attention due to their advantages of automation, repeatability, and scalability.…”

Section: Related Workmentioning

confidence: 99%

A Requirement Quality Assessment Method Based on User Stories

Dou

Qian

et al. 2023

Electronics

View full text Add to dashboard Cite

Agile development processes based on user stories often face issues such as incomplete, inconsistent, and inaccurate user requirements, which increase the workload of agile development teams and reduce the efficiency of product function development, ultimately resulting in the inability to respond quickly to user requirements. This paper proposes a user requirement quality assessment method based on user stories to address these problems. This method relies on the agile development process, constructs a user requirement quality assessment framework, defines a user story model and a user requirement quality model, develops seven user requirement quality assessment criteria, and designs a user requirement quality assessment process. A data experiment exploring the development of smartphone requirements is used to validate the feasibility and effectiveness of the method. The experimental results demonstrate that the method improves user requirement quality to some extent, providing an automated solution for agile development teams to enhance user requirement quality.

show abstract

Section: Related Workmentioning

confidence: 99%

A Requirement Quality Assessment Method Based on User Stories

Dou

Qian

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…However, when a high level of input variable correlation is present, it is harder to identify representative classes, which may result in a large number of test cases. The application of machine and deep learning models for black-box testing has also been attempted recently, such as recurrent neural networks by [21] and machine learning methods by [22] (for detecting code smells) and [23] (for simulating domain expert knowledge).…”

Section: A Cause-effect Graphs As a Black-box Testing Techniquementioning

confidence: 99%

Forward-Propagation Approach for Generating Feasible and Minimum Test Case Suites From Cause-Effect Graph Specifications

et al. 2022

View full text Add to dashboard Cite

Cause-effect graphs are a popular black-box testing technique. The most commonly used approach for generating test cases from cause-effect graph specifications uses backward propagation of forced effect activations through the graph in order to get the values of causes for the desired test case. Many drawbacks have been identified when using this approach for different testing requirements. Several algorithms for automatically generating test case suites from cause-effect graph specifications have been proposed. However, many of these algorithms do not solve the main drawbacks of the initial backpropagation approach and offer only minor improvements for specific purposes. This work proposes two new algorithms for deriving test cases from cause-effect representationsan algorithm that uses forwardpropagation of all possible cause values in order to get all feasible test cases and an algorithm that minimizes the feasible test case suite while taking multiple effect activations into account. Cause-effect graphs previously used for testing algorithms based on the backward-propagation approach were used for evaluation and comparison, as well as the newly introduced test effect coverage metric and fault detection rate effectiveness. The evaluation shows that the proposed algorithms work in real time even for a very large number of cause nodes. The results also indicate that the first newly proposed algorithm generates a larger test case suite, whereas the second newly proposed algorithm generates a smaller test case subset than the originally proposed approaches while ensuring the maximum effect coverage, fault detection rate effectiveness and a better test effect coverage ratio.INDEX TERMS Black-box testing, cause-effect graph, forward-propagation algorithm, software quality, test suite generation.

show abstract

“…However, and considering the huge amount of requirements statements already available in different specifications and standards, there is not yet a common approach to properly infer requirements patterns. Some initial attempts using machine learning [20] have been done but approaching the problem from the perspective of quality and learning from a labeled dataset. The Reuse Knowledge Group has also investigated an approach to obtain text patterns automatically [9].…”

Section: State Of the Artmentioning

confidence: 99%

Genetic Algorithms: A Practical Approach to Generate Textual Patterns for Requirements Authoring

et al. 2021

Self Cite

View full text Add to dashboard Cite

The writing of accurate requirements is a critical factor in assuring the success of a project. Text patterns are knowledge artifacts that are used as templates to guide engineers in the requirements authoring process. However, generating a text pattern set for a particular domain is a time-consuming and costly activity that must be carried out by specialists. This research proposes a method of automatically generating text patterns from an initial corpus of high-quality requirements, using genetic algorithms and a separate-and-conquer strategy to create a complete set of patterns. Our results show this method can generate a valid pattern set suitable for requirements authoring, outperforming existing methods by 233%, with requirements ratio values of 2.87 matched per pattern found; as opposed to 1.23 using alternative methods.

show abstract

Application of machine learning techniques to the flexible assessment and improvement of requirements quality

Cited by 7 publications

References 55 publications

A Requirement Quality Assessment Method Based on User Stories

A Requirement Quality Assessment Method Based on User Stories

Forward-Propagation Approach for Generating Feasible and Minimum Test Case Suites From Cause-Effect Graph Specifications

Genetic Algorithms: A Practical Approach to Generate Textual Patterns for Requirements Authoring

Contact Info

Product

Resources

About