Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Computer Sciences
Deep Learning For Coverage-Guided Fuzzing: How Far Are We?, Siqi Li, Xiaofei Xie, Yun Lin, Yuekang Li, Ruitao Feng, Xiaohong Li, Weimin Ge, Jin Song Dong
Deep Learning For Coverage-Guided Fuzzing: How Far Are We?, Siqi Li, Xiaofei Xie, Yun Lin, Yuekang Li, Ruitao Feng, Xiaohong Li, Weimin Ge, Jin Song Dong
Research Collection School Of Computing and Information Systems
Fuzzing is a widely-used software vulnerability discovery technology, many of which are optimized using coverage-feedback. Recently, some techniques propose to train deep learning (DL) models to predict the branch coverage of an arbitrary input owing to its always-available gradients etc. as a guide. Those techniques have proved their success in improving coverage and discovering bugs under different experimental settings. However, DL models, usually as a magic black-box, are notoriously lack of explanation. Moreover, their performance can be sensitive to the collected runtime coverage information for training, indicating potentially unstable performance. In this work, we conduct a systematic empirical study on …
A Model-Based Ai-Driven Test Generation System, Dionny Santiago
A Model-Based Ai-Driven Test Generation System, Dionny Santiago
FIU Electronic Theses and Dissertations
Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. …
Does The Test Work? Evaluating A Web-Based Language Placement Test, Avizia Long, Sun-Young Shin, Kimberly Geeslin, Erik Willis
Does The Test Work? Evaluating A Web-Based Language Placement Test, Avizia Long, Sun-Young Shin, Kimberly Geeslin, Erik Willis
Faculty Publications
In response to the need for examples of test validation from which everyday language programs can benefit, this paper reports on a study that used Bachman’s (2005) assessment use argument (AUA) framework to examine evidence to support claims made about the intended interpretations and uses of scores based on a new web-based Spanish language placement test. The test, which consisted of 100 items distributed across five item types (sound discrimination, grammar, listening comprehension, reading comprehension, and vocabulary), was tested with 2,201 incoming first-year and transfer students at a large, Midwestern public university. Analyses of internal consistency and validity revealed the …