Treffer: JDFD- Java data flow detection tool

Title:
JDFD- Java data flow detection tool
Authors:
Valli, Sruthi Ganesan
Publisher Information:
Texas A&M University- Kingsville
Publication Year:
2018
Collection:
Texas A&M University-Kingsville: AKM Digital Repository
Subject Terms:
Computer science, Software engineering -- Testing, Data flow computing, Java (computer program language)
Document Type:
other/unknown material
File Description:
pdf; 2544046 bytes
Language:
English
Relation:
http://cdm16771.contentdm.oclc.org/u?/p16771coll2,352
Availability:
http://cdm16771.contentdm.oclc.org/u?/p16771coll2,352
Rights:
The right to download or print any of the pages of this thesis (Material) is granted by the copyright owner only for personal or classroom use. The author retains all proprietary rights, including copyright ownership. Any reproduction or editing or other use of this Material by any means requires the express written permission of the copyright owner. Except as provided above, or any use beyond what is allowed by fair use (Title 17 Section 107 U.S.C.), you may not reproduce, republish, post, transmit or distribute any Material from this web site in any physical or digital form without the permission of the copyright owner of the Material. Inquiries regarding any further use of these materials should be addressed to Administration, Jernigan Library, Texas A&M University-Kingsville, 700 University Blvd. Kingsville, Texas 78363-8202, (361)593-3416.
Accession Number:
edsbas.12BEE7E3
Database:
BASE

Weitere Informationen

Testing is vital for effective software development. Object-oriented programs however cause unique issues in testing by introducing anomalous behavior in software due to inheritance and polymorphism. Generics allows parameters for a method to be inferred with diverse types causing behavioral anomaly among classes in an inheritance hierarchy. This research presents JDFD, a web-based tool built on top of Soot which identifies potential sources of anomaly due to object-orientation and generics using static call graph, data-flow and type-flow graphs. JDFD is applied to the Employee program and the ResultCheck program case studies which exhibit behavioral anomalies, to verify the effectiveness of the graph visualizations in identifying the potential sources of faults or anomalies present in the program. It was found that by backtracking through data flow and call graphs the source of anomaly due to polymorphic calls could be identified. Using the type-flow graph, the source of anomaly due to generics could be identified effectively. This tool can be extended to include dynamic sync between source code and the visualizations. It could be further extended to identify security leaks in source code.