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Applying Machine Learning Techniques to the Flexible Assessment of Requirements Quality

Applying machine learning techniques to requirements quality

 

When

Tuesday, September 24, 2019, 4:00 PM CET (Madrid)/ 11:00 PM JST (Tokyo)/ Sept 25, 12:00 AM AEDT (Sidney)/ 7:00 AM PST (Los Angeles)

Friday, September 27, 2019, 9:00 AM CET (Madrid)/ 4:00 PM JST (Tokyo)/ 5:00 PM AEDT (Sidney)/ 12:00 AM PST (Los Angeles)

 

Description

In the world of systems engineering, the importance of having high quality requirements is well known and that is why there are standards and guidelines that establish the characteristics that the requirements must have for considering them of good quality.

To obtain quality measurements of the requirements it is common to use quantitative quality metrics based on established standards. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. This webinar is focused on the presentation of a flexible method to assess and improve the quality of requirements that can be easily adapted to different contexts, projects, organizations and quality standards, with a high degree of automation.

In the method proposed, the domain experts contribute with an initial set of requirements that they have classified according to their quality, and their quality metrics are extracted. Then machine learning techniques are used to emulate the implicit expert’s quality function. A procedure to suggest least-effort improvements in bad requirements is also provided.

The method is easily tailorable to different contexts, different styles to write requirements, and different demands in quality. The whole process of inferring and applying the quality rules adapted to each organization is highly automated.

 

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