PhD Proposal by Qianqian Wang

Event Details
  • Date/Time:
    • Wednesday December 5, 2018 - Thursday December 6, 2018
      3:15 am - 4:59 am
  • Location: Klaus 2100
  • Phone:
  • URL:
  • Email:
  • Fee(s):
  • Extras:
No contact information submitted.

Summary Sentence: Improving In-House Testing Using Field Execution Data

Full Summary: No summary paragraph submitted.

Title: Improving In-House Testing Using Field Execution Data


Qianqian Wang

Ph.D. student in Computer Science

School of Computer Science

College of Computing

Georgia Institute of Technology



Date: Wednesday, December 5, 2018

Time: 3:15pm-5pm (EST)

Location: Klaus 2100



Prof. Alessandro Orso (Advisor), School of Computer Science, Georgia Institute of Technology

Prof. Vivek Sarkar, School of Computer Science, Georgia Institute of Technology

Dr. Spencer Rugaber, School of Computer Science, Georgia Institute of Technology

Prof. Yuriy Brun, College of Information & Computer Science, University of Massachusetts




Software testing is today the most widely used approach for assessing and improving software quality. Despite its popularity, however, software testing has a number of inherent limitations. First, due to resource limitations, in-house tests necessarily exercise only a tiny fraction of all the possible behaviors of a software system. Second, testers typically select this fraction of behaviors to be tested based either on some (more or less rigorous) selection criteria or on their assumptions, intuition, and experience. As a result, in-house tests are typically not representative of the software behavior exercised by real users, which ultimately results in the software behaving incorrectly and failing in the field, after it has been released. 


To address this problem, and improve the effectiveness of in-house testing, I propose a set of techniques for measuring and bridging the gap between in-house tests and field executions. My first technique allows for quantifying and analyzing the differences between behaviors exercised in-house and in the field. My second approach leverages the differences identified by my first technique to generate test inputs that mimic field behaviors and can be added to existing in-house test suites. This approach uses a guided symbolic analysis. Finally, my third approach leverages the state observed in the field to improve symbolic input generation and make test generation more effective.

Additional Information

In Campus Calendar

Graduate Studies

Invited Audience
Faculty/Staff, Public, Graduate students, Undergraduate students
Phd proposal
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 30, 2018 - 11:29am
  • Last Updated: Nov 30, 2018 - 11:29am