Although machine learning (ML) has been successful in automating various software engineering needs, software testing still remains a highly challenging topic. In this paper, we aim to improve the generative testing of software by directly augmenting the random number generator (RNG) with a deep reinforcement learning (RL) agent using an efficient, automatically extractable state representation of the software under test. Using the Cosmos SDK as the testbed, we show that the proposed DeepRNG framework provides a statistically significant improvement to the testing of the highly complex software library with over 350,000 lines of code. The source code of the DeepRNG framework is publicly available online.
UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach
National Economics University（国立经济大学）；Norwegian University of Science and Technology（挪威科技大学）；Waseda University（早稻田大学）；Duy Tan University（杜维谭大学）；University of Luxembourg（卢森堡大学）；National Academy of Education Management（国家教育管理学院）
ISTQB Certifications Under the Lens: Their Contributions to the Software-Testing Profession; and AI-assisted Synthesis of Practitioners' Endorsements and Criticisms
Queen’s University Belfast（贝尔法斯特女王大学）；Testinium A.Ş.（Testinium股份有限公司）；Q Investment Bank（Q投资银行）；Azerbaijan Technical University（阿塞拜疆理工大学）
LLM-Assisted Model-Based GUI Testing for Vue.js Web Applications
Macau University of Science and Technology（澳门科技大学）；Beihang University（北京航空航天大学）；Macau University of Science and Technology Zhuhai MUST Science and Technology Research Institute（澳门科技大学珠海MUST科学研究院）；University of Nottingham Ningbo China（宁波诺丁汉大学）；Dalian Maritime University（大连海事大学）；University of Tokyo（东京大学）；University of Alberta（阿尔伯塔大学）
With the rapid growth in the number of IoT devices being added to the network, a major concern that arises is the security of these systems. As these devices are resource constrained, safety measures are difficult to implement on the edge. We propose a novel approach for the detection of IoT device attacks based on the use of formal modeling and mutation testing. Namely, we model the behavior of small IoT devices such as motion sensors and RFID reader as state machines with timeouts. We also model basic IoT attacks; namely, battery draining, sleep deprivation, data falsification, replay, and m…
Queen’s University Belfast（贝尔法斯特女王大学）；University of Innsbruck（因斯布鲁克大学）；Blekinge Institute of Technology（布莱金厄理工学院）
Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques. Moreover, this can …
The malfunction of safety-critical systems may cause damage to people and the environment. Software within those systems is rigorously designed and verified according to domain specific guidance, such as ISO26262 for automotive safety. This paper describes academic and industrial co-operation in tool development to support one of the most stringent of the requirements --- achieving full code coverage in requirements-driven testing. We present a verification workflow supported by a tool that integrates the coverage measurement tool RapiCover with the test-vector generator FShell. The tool assis…
Ten Simple Rules for AI-Assisted Coding in Science
Yonsei University（延世大学）；Stanford University（斯坦福大学）；Princeton University（普林斯顿大学）；University of Science and Technology of China（中国科学技术大学）；University of California, Irvine（加州大学尔湾分校）
Despite its effectiveness in uncovering software defects, American Fuzzy Lop (AFL), one of the best grey-box fuzzers, is inefficient when fuzz-testing source-unavailable programs. AFL's binary-only fuzzing mode, QEMU-AFL, is typically 2-5X slower than its source-available fuzzing mode. The slowdown is largely caused by the heavy dynamic instrumentation. Recent fuzzing techniques use Intel Processor Tracing (PT), a light-weight tracing feature supported by recent Intel CPUs, to remove the need of dynamic instrumentation. However, we found that these PT-based fuzzing techniques are even slower t…
