2020
DOI: 10.1007/s11219-020-09511-4
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Application of machine learning techniques to the flexible assessment and improvement of requirements quality

Abstract: Context: It is already common to compute quantitative metrics of requirements to assess their quality. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. Specifically, we show that a linear combination of metrics is insufficient to adequately compute a global measure of quality.Objective: In this work we propose to develop a flexible method to assess and improve the quality of requirements that can be adapted to diffe… Show more

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Cited by 8 publications
(7 citation statements)
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References 55 publications
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“…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%
“…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%
“…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%
“…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%