Research
     
 
Development and Empirical Evaluation of checkVT: A Browser Add-on for Verifying the Safety of URLs
 
 
  Student Achievement Recognition (Fall'20): Emyll Almonte, an Undergraduate IT Major at Montclair State University, has developed and successfully published a browser add-on called checkVT:

https://addons.mozilla.org/en-US/firefox/addon/checkvt/
cvt4  
  checkVT is a simple web browser extension that takes a selected URL via context-menu and submits it directly to be checked against all engines on VirusTotal with an added feature. The added feature in checkVT is basically the part of the process that tries to find the effective URL (redirect) if it exists on the URL that was submitted, and sends that URL to VirusTotal rather than the URL that was selected. This extra step helps users see VirusTotal results for the URL host that they would have ended up at, as opposed to the original link, which happens with most phishing links. Additional information can be found here:
https://github.com/ealmonte32/checkVT
 
     
 
Using Maching Learning to Automate the Procedures Involved in Requirements Inspections

 
  ml4Requirements 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.

het9  
  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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"In a humble state, you learn better. I can't find anything else very exciting about humility, but at least there's that." ~ John Dooner

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