Visible to the public Predicting the Difficulty of Compromise through How Attackers Discover VulnerabilitiesConflict Detection Enabled

Submitted by gargvaibhav on Sun, 09/25/2022 - 11:55am

PI(s), Co-PI(s), Researchers:

PI: Andrew Meneely; Co-PI: Laurie Williams; Researchers: Ben Meyers and Nasif Imtiaz

HARD PROBLEM(S) ADDRESSED
This refers to Hard Problems, released November 2012.

  • Metrics

PUBLICATIONS
Papers were written as a result of your research from the current quarter only.

KEY HIGHLIGHTS

  • We are wrapping up our systematic literature review of 53 research studies about human errors in software engineering
  • We revised our apologies mining study and prepared a journal submission based on it.
  • We have begun a user study of our Taxonomy of Human Errors in Software Engineering (T.H.E.S.E.). We are meeting weekly with participants over 8 weeks who are journaling their software engineering-related human errors each week, classifying it according to the T.H.E.S.E. taxonomy. This taxonomy was developed from combining the systematic literature review and the mining study. The goal of the user study is to improve the usability and disambiguity of the taxonomy, and to qualitatively uncover any insights. This work is part of the last stretch of work by Ben Meyers for his dissertation research, where he expects to finish in Spring 2023.

Each effort should submit one or two specific highlights. Each item should include a paragraph or two along with a citation if available. Write as if for the general reader of IEEE S&P.
The purpose of the highlights is to give our immediate sponsors a body of evidence that the funding they are providing (in the framework of the SoS lablet model) is delivering results that "more than justify" the investment they are making.

COMMUNITY ENGAGEMENT

EDUCATIONAL ADVANCES:

  • None.
Groups: