Research


Research

	Using Maching Learning to Automate the Procedures Involved in Requirements Inspections
	Requirements inspections involve multiple inspectors independently reviewing a requirements document and reporting faults in the document. But, inspectors report both faults and non-faults (false-positives). We are using machine learning based approaches to validate requirements reviews. Our approach uses supervised machine learning algorithms to isolate faults from false-positives. An important feature that we use for training our classifiers is labeling our review data with the fault-types (ambiguity, inconsistent, incorrect requirements, omission, etc.). More details and publications related to this research project can be found at the following links: https://www.researchgate.net/project/Machine-Learning-in-Requirement-Inspections http://vaibhavanu.com/VBF-TP-001.html
	Using Human Error & Human Factors Research to Improve Software Requirements Quality
	This research employs the Cognitive Psychology research on human errors to address a serious problem in Software Engineering: defects made during software development. We propose that because software development is a human-centric process, most software defects can be traced back to failures of human cognition (also called human errors or mental errors). In order to have the greatest impact on software quality and to minimize the impact of defects, our research is focused on the earliest phase of software development: the requirements engineering phase.
	The major goal of this research effort is to use insights from Cognitive Psychology research on human errors to develop and empirically validate : (1) a *taxonomy* of requirements phase human errors, and (2) requirements defect detection techniques and tools based on the taxonomy.
	Our research group has organized workshops in premier Software Engineering conferences to elicit instances of human errors that happen in requirements engineering practice in the industry. Experimental and training documents related to this research: vaibhavanu.com/NDSU-CS-TP-2017-001.html vaibhavanu.com/NDSU-CS-TP-2016-001.html vaibhavanu.com/SDP_Inspection.html humanerrorinse.org/Studies/2015/Fall_NDSU/ humanerrorinse.org/Studies/2015/Fall_NDSU_Experiment_1




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