Adaptive Regression Testing (ART) Strategies
As software systems evolve, the types of maintenance
activities that are applied to them change.
Differences in versions can involve different amounts and
types of code modifications, and this can affect the costs
and benefits of regression testing techniques in different ways.
It follows that across system lifetimes, there may
be no single regression testing technique that is the
most cost-effective technique to use on every version.
To date, many regression testing techniques have
been proposed, but only a little research has been done
on the problem of helping practitioners choose appropriate
techniques under particular system and process constraints.
Further, no research has considered strategies for
automatically selecting techniques to use on new
versions as systems evolve.
This project addresses this by creating and empirically
studying adaptive regression testing (ART) strategies.
ART strategies are approaches that operate across system
lifetimes, and attempt to identify the regression testing
techniques that will be the most cost-effective for each
regression testing session.
In particular, we investigate Analytical Hierarchy
Process (AHP) methods; these have been used in multiple
criteria decision making processes on complex problems
in areas such as agriculture, civil engineering, and
software engineering but no research has addressed
the possibility of incorporating AHP methods into
regression testing strategies.
Graduate Researcher: Md. Junaid Arafeen and Md. Hossain