Wednesday, May 6, 2009
Kinga Dobolyi
Chair: Mary Lou Soffa; John Knight; William Wulf; Chad Dodson
Advisor: Westley Weimer
OLSSON 228E, 15:30:00
A Ph.D. Proposal
An Exploration of Errors in Web-based Applications in the Context of Web-based
ABSTRACT
Web-based applications are one of the most widely used types of software, and have become the backbone of the e-commerce and communications businesses. These applications are often mission-critical for many organizations, but they generally suffer from low customer loyalty. Although such concerns would normally motivate the need for highly reliable and well-tested systems, web-based applications are subject to further constraints in their development lifecycles that often preclude complete testing.
To address these concerns, this research will explore web-based application errors in the context of web-based application testing. The main thesis of this research is that web-based application errors have special properties that can be exploited to improve the current state of web-based application testing and development. This proposed research will result in precise, automated approaches to the regression testing of web-based applications that reduce the cost of such testing, making its adoption more feasible for developers. Additionally, I propose to construct a model of web application fault severity, backed by a human study, to validate or refute the current underlying assumption of fault severity uniformity in defect seeding for this domain, propose software engineering guidelines to avoid high severity faults, and facilitate testing techniques in find high-severity faults.
Studying fault severities from the customer perspective is a novel contribution to the web application testing field. This research will approach testing web-based applications by recognizing that errors in web-based applications can be successfully modeled due to the tree-structured nature of XML/HTML output, that unrelated web-based applications fail in similar ways, and that these failures can be modeled according to their customer-perceived severities.