arXiv subjects

Dave Towey

Publications and source records attributed to Dave Towey.

At least 19 recordsLinked to original sources

arXiv2026

LLM-Assisted Model-Based GUI Testing for Vue.js Web Applications

Vue.js is a popular framework for building modern web applications. As Vue.js functionality and tooling support grow, ensuring its reliability (through automated testing) is becoming increasingly important. Although model-based testing has been successfully used to automate graphical user interface (GUI) testing on other platforms, its application to Vue.js remains challenging: Transition candidates, which are spread across router configurations and single-file components (SFCs), must be concretized and normalized into an executable page transition graph (PTG) for testing. To address this, we propose the LLMVue framework, which uses a large language model (LLM) to generate a PTG from Vue.js source code. LLMVue infers component hierarchies and route transitions, merging them into a unified PTG across multiple SFCs. We evaluated LLMVue on a collection of ten open-source Vue.js projects from GitHub, using GPT-4o as the LLM backbone. The constructed graphs demonstrate high precision and recall, with low graph edit distance. LLMVue -guided testing also significantly improves the coverage and exploration efficiency, compared to a random exploration baseline (with the same time constraints). To the best of our knowledge, this is the first use of LLMs for model-based GUI testing of Vue.js applications using source-level PTG extraction.

cs.SE↗

arXiv2026

ATGBuilder: Feature-Assisted Graph Learning for Activity Transition Graph Construction with Seed Supervision

Android applications are organized around activities that provide visual Graphical User Interface (GUI) containers that host the UI and handle user interaction events. Activity Transition Graphs (ATGs) have been widely used to model apps' GUI navigation. However, the construction of high-quality ATGs is challenging: ATGs based on static analysis may miss acceptable transitions and may extract infeasible ones; while dynamically explored ATGs can yield incomplete transitions. Recent learning-based approaches can treat ATG construction as a seed-supervised link-prediction task. However, the use of activity-layout and widget-trigger information for ATG construction remains limited. We propose ATGBuilder, a feature-assisted graph-learning approach for seed-supervised ATG construction. ATGBuilder uses a Large Language Model (LLM) to summarize UI activity metadata from layouts into compact textual functionality summaries. ATGBuilder explicitly models widget-trigger information into the edge attribute: It then uses an auxiliary widget-attribute reconstruction objective on this information during model training. ATGBuilder's performance was evaluated across a series of ablations on the frontmatter corpus, and an experiment on benchmark using manually-checked ground-truth ATGs. Experiments on multiple benchmarks show that ATGBuilder significantly outperforms state-of-the-art methods. We further demonstrate its effectiveness by improving automated GUI exploration tools through better navigation guidance.

cs.SE↗

arXiv2026

LLM-Based Static Verification of Code Against Natural-Language Requirements: An Industrial Experience Report

Large language models (LLMs) are increasingly used to generate requirements specifications, design documents, code, and test cases. In contrast, much less attention has been given to a more difficult assurance problem: statically verifying whether implemented code satisfies requirements written in natural language. Conventional static analysis tools are effective at detecting coding defects and known vulnerability patterns, but they cannot determine whether program behavior matches intended business logic. Detecting such defects requires reasoning over the specification rather than the code alone. Software testing can expose some of these mismatches, but its effectiveness depends heavily on test design, executable artifacts, and runtime environments. This article presents a two-stage LLM-based workflow for addressing this challenge in an intelligent-vehicle cybersecurity case study. In the first stage, an AI-based rule miner extracts verifiable rules from natural-language requirements while explicitly identifying ambiguity, self-contradiction, and other non-verifiable statements. In the second stage, an AI-based code auditor checks implementation evidence against the extracted rules. Instead of asking a single LLM to directly verify code against lengthy natural-language specifications, the workflow introduces a structured intermediate representation to reduce hallucination, output variability, limited explainability, and context loss. The resulting approach is a requirement-aware and semantics-aware form of static analysis that complements software testing. By analyzing requirements and source code without requiring compilation, execution, or runtime environments, the method shifts verification and validation activities left in the development lifecycle. This LLM-based static analysis is also a new approach to addressing the test oracle problem.

cs.SE↗

arXiv2026

Short-term electricity load forecasting with multi-frequency reconstruction diffusion

Diffusion models have emerged as a powerful method in various applications. However, their application to Short-Term Electricity Load Forecasting (STELF) -- a typical scenario in energy systems -- remains largely unexplored. Considering the nonlinear and fluctuating characteristics of the load data, effectively utilizing the powerful modeling capabilities of diffusion models to enhance STELF accuracy remains a challenge. This paper proposes a novel diffusion model with multi-frequency reconstruction for STELF, referred to as the Multi-Frequency-Reconstruction-based Diffusion (MFRD) model. The MFRD model achieves accurate load forecasting through four key steps: (1) The original data is combined with the decomposed multi-frequency modes to form a new data representation; (2) The diffusion model adds noise to the new data, effectively reducing and weakening the noise in the original data; (3) The reverse process adopts a denoising network that combines Long Short-Term Memory (LSTM) and Transformer to enhance noise removal; and (4) The inference process generates the final predictions based on the trained denoising network. To validate the effectiveness of the MFRD model, we conducted experiments on two data platforms: Australian Energy Market Operator (AEMO) and Independent System Operator of New England (ISO-NE). The experimental results show that our model consistently outperforms the compared models.

cs.LG↗

arXiv2025

LLMCFG-TGen: Using LLM-Generated Control Flow Graphs to Automatically Create Test Cases from Use Cases

Appropriate test-case generation is critical in software testing and significantly impacts testing quality. Requirements-Based Test Generation (RBTG) derives test cases from software requirements to verify whether system behavior aligns with user needs and expectations. Requirements are often documented in Natural Language (NL), with use-case descriptions being a popular method for capturing functional behaviors and interaction flows in a structured, readable form. Recently, Large Language Models (LLMs) have shown strong potential for automating test generation from NL requirements. However, existing LLM-based approaches often fail to ensure comprehensive and non-redundant coverage, and may not adequately capture complex conditional logic, leading to incomplete test cases. To address these limitations, we propose an end-to-end approach called Test Generation based on LLM-generated Control Flow Graphs (LLMCFG-TGen), which generates test cases from NL use-case descriptions. It consists of three steps: (1) CFG Generation, where an LLM transforms a use case into a structured JSON-based Control Flow Graph capturing all potential branches; (2) Test-Path Extraction, where the CFG is traversed to derive execution paths; and (3) Test-Case Creation, where test cases are generated from these paths. We evaluate the approach on six use-case datasets across diverse domains. Results show that LLMs can effectively construct structured CFGs from NL use cases. Compared with two baselines, LLMCFG-TGen produces more complete and structurally consistent test cases by better capturing behavioral logic and execution flows. Both LLM-based and practitioner-based evaluations further confirm improved comprehensiveness and logical coherence while reducing manual effort.

cs.SE↗

arXiv2025

Topological Structure Description for Artcode Detection Using the Shape of Orientation Histogram

The increasing ubiquity of smartphones and resurgence of VR/AR techniques, it is expected that our everyday environment may soon be decorating with objects connecting with virtual elements. Alerting to the presence of these objects is therefore the first step for motivating follow-up further inspection and triggering digital material attached to the objects. This work studies a special kind of these objects -- Artcodes -- a human-meaningful and machine-readable decorative markers that camouflage themselves with freeform appearance by encoding information into their topology. We formulate this problem of recongising the presence of Artcodes as Artcode proposal detection, a distinct computer vision task that classifies topologically similar but geometrically and semantically different objects as a same class. To deal with this problem, we propose a new feature descriptor, called the shape of orientation histogram, to describe the generic topological structure of an Artcode. We collect datasets and conduct comprehensive experiments to evaluate the performance of the Artcode detection proposer built upon this new feature vector. Our experimental results show the feasibility of the proposed feature vector for representing topological structures and the effectiveness of the system for detecting Artcode proposals. Although this work is an initial attempt to develop a feature-based system for detecting topological objects like Artcodes, it would open up new interaction opportunities and spark potential applications of topological object detection.

cs.CV↗

arXiv2025

Requirements-Based Test Generation: A Comprehensive Survey

As an important way of assuring software quality, software testing generates and executes test cases to identify software failures. Many strategies have been proposed to guide test-case generation, such as source-code-based approaches and methods based on bug reports. Requirements-based test generation (RBTG) constructs test cases based on specified requirements, aligning with user needs and expectations, without requiring access to the source code. Since its introduction in 1994, there have been many contributions to the development of RBTG, including various approaches, implementations, tools, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on RBTG, categorizing requirement types, classifying approaches, investigating types of test cases, summarizing available tools, and analyzing experimental evaluations. This paper also summarizes the domains and industrial applications of RBTG, and discusses some open research challenges and potential future work.

cs.SE↗

arXiv2025

LRASGen: LLM-based RESTful API Specification Generation

REpresentation State Transfer (REST) is an architectural style for designing web applications that enable scalable, stateless communication between clients and servers via common HTTP techniques. Web APIs that employ the REST style are known as RESTful (or REST) APIs. When using or testing a RESTful API, developers may need to employ its specification, which is often defined by open-source standards such as the OpenAPI Specification (OAS). However, it can be very time-consuming and error-prone to write and update these specifications, which may negatively impact the use of RESTful APIs, especially when the software requirements change. Many tools and methods have been proposed to solve this problem, such as Respector and Swagger Core. OAS generation can be regarded as a common text-generation task that creates a formal description of API endpoints derived from the source code. A potential solution for this may involve using Large Language Models (LLMs), which have strong capabilities in both code understanding and text generation. Motivated by this, we propose a novel approach for generating the OASs of RESTful APIs using LLMs: LLM-based RESTful API-Specification Generation (LRASGen). To the best of our knowledge, this is the first use of LLMs and API source code to generate OASs for RESTful APIs. Compared with existing tools and methods, LRASGen can generate the OASs, even when the implementation is incomplete (with partial code, and/or missing annotations/comments, etc.). To evaluate the LRASGen performance, we conducted a series of empirical studies on 20 real-world RESTful APIs. The results show that two LLMs (GPT-4o mini and DeepSeek V3) can both support LARSGen to generate accurate specifications, and LRASGen-generated specifications cover an average of 48.85% more missed entities than the developer-provided specifications.

cs.SE↗

arXiv2025

Integrating Artificial Intelligence with Human Expertise: An In-depth Analysis of ChatGPT's Capabilities in Generating Metamorphic Relations

Context: This paper provides an in-depth examination of the generation and evaluation of Metamorphic Relations (MRs) using GPT models developed by OpenAI, with a particular focus on the capabilities of GPT-4 in software testing environments. Objective: The aim is to examine the quality of MRs produced by GPT-3.5 and GPT-4 for a specific System Under Test (SUT) adopted from an earlier study, and to introduce and apply an improved set of evaluation criteria for a diverse range of SUTs. Method: The initial phase evaluates MRs generated by GPT-3.5 and GPT-4 using criteria from a prior study, followed by an application of an enhanced evaluation framework on MRs created by GPT-4 for a diverse range of nine SUTs, varying from simple programs to complex systems incorporating AI/ML components. A custom-built GPT evaluator, alongside human evaluators, assessed the MRs, enabling a direct comparison between automated and human evaluation methods. Results: The study finds that GPT-4 outperforms GPT-3.5 in generating accurate and useful MRs. With the advanced evaluation criteria, GPT-4 demonstrates a significant ability to produce high-quality MRs across a wide range of SUTs, including complex systems incorporating AI/ML components. Conclusions: GPT-4 exhibits advanced capabilities in generating MRs suitable for various applications. The research underscores the growing potential of AI in software testing, particularly in the generation and evaluation of MRs, and points towards the complementarity of human and AI skills in this domain.

cs.SE↗

arXiv2024

A Survey on Web Application Testing: Over a Decade of Evolution

As one of the most popular software applications, a web application is a program accessible through the web that dynamically generates content based on user interactions or contextual data; examples include online shopping platforms, social networking sites, and financial services. Web applications operate in diverse environments and leverage web technologies such as HTML, CSS, JavaScript, and Ajax, often incorporating features like asynchronous operations to enhance user experience. Due to the growing number of users and the popularity of web applications, the quality of these applications has become increasingly important. Web Application Testing (WAT) plays a vital role in ensuring the functionality, security, and reliability of web applications. Given the speed with which web technologies are evolving, WAT is especially important. In the last twelve years, various WAT approaches have been developed. The diversity of approaches reflects the many aspects of web applications, such as dynamic content, asynchronous operations, and diverse user environments. This paper provides a comprehensive overview of the main achievements over the last twelve years: It examines the main steps involved in WAT, including test case generation and execution, as well as evaluation and assessment. The currently available tools for WAT are also examined. The paper also discusses open research challenges and potential future work in WAT.

cs.SE↗

arXiv2024

Ribonucleic-Acid Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey

The interaction between Ribonucleic Acids (RNAs) and proteins, also called RNA Protein Interaction (RPI), governs biological processes, including gene regulation and disease pathogenesis. This comprehensive survey examines Artificial Intelligence (AI) applications in Deep Learning-based RPI Prediction (DL-based RPIP) through eight Research Questions (RQs), analyzing 179 studies (2014--2023). The key findings include: sustained technical evolution through embryonic (2014--2017), accelerated (2018--2022), and expansion phases (2023) (RQ1); hybrid models integrating Graph Neural Networks (GNNs) (for topological interface modeling) and Transformers (for long-range dependencies) achieve state-of-the-art performance (RQ4); pretrained language models enhance small-sample learning, but the cross-species generalization declines sharply with evolutionary distance (RQ5). Critical challenges persist, including data heterogeneity across databases, the scarcity of standardized benchmarks (RQ2), and balancing the trade-off between feature encoding and information preservation (RQ3). Future advancements require biologically informed DL architectures, multi-feature fusion, and rigorous cross-validation to bridge the generalization-interpretability gap (RQ8): This would accelerate the clinical translation of predictive tools (RQ6/RQ7). As the first comprehensive analysis spanning feature encoding, modeling, evaluation, applications, and tools, this work fills a critical gap in the DL-based RPIP literature.

q-bio.QM↗

arXiv2024

Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey

Short-Term Electricity-Load Forecasting (STELF) refers to the prediction of the immediate demand (in the next few hours to several days) for the power system. Various external factors, such as weather changes and the emergence of new electricity consumption scenarios, can impact electricity demand, causing load data to fluctuate and become non-linear, which increases the complexity and difficulty of STELF. In the past decade, deep learning has been applied to STELF, modeling and predicting electricity demand with high accuracy, and contributing significantly to the development of STELF. This paper provides a comprehensive survey on deep-learning-based STELF over the past ten years. It examines the entire forecasting process, including data pre-processing, feature extraction, deep-learning modeling and optimization, and results evaluation. This paper also identifies some research challenges and potential research directions to be further investigated in future work.

cs.LG↗

arXiv2024

Metamorphic Relation Generation: State of the Art and Visions for Future Research

Metamorphic testing has become one mainstream technique to address the notorious oracle problem in software testing, thanks to its great successes in revealing real-life bugs in a wide variety of software systems. Metamorphic relations, the core component of metamorphic testing, have continuously attracted research interests from both academia and industry. In the last decade, a rapidly increasing number of studies have been conducted to systematically generate metamorphic relations from various sources and for different application domains. In this article, based on the systematic review on the state of the art for metamorphic relations' generation, we summarize and highlight visions for further advancing the theory and techniques for identifying and constructing metamorphic relations, and discuss potential research trends in related areas.

cs.SE↗

arXiv2024

Large Language Models for Automated Web-Form-Test Generation: An Empirical Study

Testing web forms is an essential activity for ensuring the quality of web applications. It typically involves evaluating the interactions between users and forms. Automated test-case generation remains a challenge for web-form testing: Due to the complex, multi-level structure of web pages, it can be difficult to automatically capture their inherent contextual information for inclusion in the tests. Large Language Models (LLMs) have shown great potential for contextual text generation. This motivated us to explore how they could generate automated tests for web forms, making use of the contextual information within form elements. To the best of our knowledge, no comparative study examining different LLMs has yet been reported for web-form-test generation. To address this gap in the literature, we conducted a comprehensive empirical study investigating the effectiveness of 11 LLMs on 146 web forms from 30 open-source Java web applications. In addition, we propose three HTML-structure-pruning methods to extract key contextual information. The experimental results show that different LLMs can achieve different testing effectiveness. Compared with GPT-4, the other LLMs had difficulty generating appropriate tests for the web forms: Their successfully-submitted rates (SSRs) decreased by 9.10% to 74.15%. Our findings also show that, for all LLMs, when the designed prompts include complete and clear contextual information about the web forms, more effective web-form tests were generated. Specifically, when using Parser-Processed HTML for Task Prompt (PH-P), the SSR averaged 70.63%, higher than the 60.21% for Raw HTML for Task Prompt (RH-P) and 50.27% for LLM-Processed HTML for Task Prompt (LH-P). Finally, this paper also highlights strategies for selecting LLMs based on performance metrics, and for optimizing the prompt design to improve the quality of the web-form tests.

cs.SE↗

arXiv2024

TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation

Artificial intelligence (AI) has revolutionized software engineering (SE) by enhancing software development efficiency. The advent of pre-trained models (PTMs) leveraging transfer learning has significantly advanced AI for SE. However, existing PTMs that operate on individual code tokens suffer from several limitations: They are costly to train and fine-tune; and they rely heavily on labeled data for fine-tuning on task-specific datasets. In this paper, we present TransformCode, a novel framework that learns code embeddings in a contrastive learning manner. Our framework is encoder-agnostic and language-agnostic, which means that it can leverage any encoder model and handle any programming language. We also propose a novel data-augmentation technique called abstract syntax tree (AST) transformation, which applies syntactic and semantic transformations to the original code snippets, to generate more diverse and robust samples for contrastive learning. Our framework has several advantages over existing methods: (1) It is flexible and adaptable, because it can easily be extended to other downstream tasks that require code representation (such as code-clone detection and classification); (2) it is efficient and scalable, because it does not require a large model or a large amount of training data, and it can support any programming language; (3) it is not limited to unsupervised learning, but can also be applied to some supervised learning tasks by incorporating task-specific labels or objectives; and (4) it can also adjust the number of encoder parameters based on computing resources. We evaluate our framework on several code-related tasks, and demonstrate its effectiveness and superiority over the state-of-the-art methods such as SourcererCC, Code2vec, and InferCode.

cs.SE↗

arXiv2024

Large Language Models for Mobile GUI Text Input Generation: An Empirical Study

Mobile apps have become essential, making quality assurance increasingly important. GUI testing is widely used for automated exploration, yet text-input components remain a major obstacle, as many UI pages require semantically appropriate text inputs before proceeding. Large Language Models have shown promise in generating context-aware text, but the effectiveness of different UI representations, feedback mechanisms, and human intervention remains unclear. This paper presents a large-scale empirical study addressing these gaps. We evaluate nine state-of-the-art LLMs across 115 real-world apps, comparing three UI-context prompting settings: extracted textual context, UI-hierarchy XML, and screenshot-based vision input. Results show extracted context and XML achieve comparable page-pass-through rates of 71.4% and 71.0%, while vision-based input reaches 65.1% but incurs substantially higher token costs. In bug-detection experiments with 37 real-world text-input bugs, LLMs generating invalid inputs detect about 51% of issues across all evaluated models. A feedback-enhanced protocol, incorporating execution outcomes into subsequent attempts, improves average PPTRs to 69.2-73.8% and raises bug-detection rates to 51.0-64.5%. Human testers further refine inputs, yielding additional gains. We integrate the process into DroidBot, augmenting its UI-exploration capabilities. We derive actionable insights on context selection, cost-effectiveness, feedback strategies, and human-LLM collaboration, advancing both knowledge and practice in Android testing.

cs.SE↗

arXiv2024

Uncovering the Metaverse within Everyday Environments: a Coarse-to-Fine Approach

The recent release of the Apple Vision Pro has reignited interest in the metaverse, showcasing the intensified efforts of technology giants in developing platforms and devices to facilitate its growth. As the metaverse continues to proliferate, it is foreseeable that everyday environments will become increasingly saturated with its presence. Consequently, uncovering links to these metaverse items will be a crucial first step to interacting with this new augmented world. In this paper, we address the problem of establishing connections with virtual worlds within everyday environments, especially those that are not readily discernible through direct visual inspection. We introduce a vision-based approach leveraging Artcode visual markers to uncover hidden metaverse links embedded in our ambient surroundings. This approach progressively localises the access points to the metaverse, transitioning from coarse to fine localisation, thus facilitating an exploratory interaction process. Detailed experiments are conducted to study the performance of the proposed approach, demonstrating its effectiveness in Artcode localisation and enabling new interaction opportunities.

cs.HC↗

arXiv2024

Toward Cost-effective Adaptive Random Testing: An Approximate Nearest Neighbor Approach

Adaptive Random Testing (ART) enhances the testing effectiveness (including fault-detection capability) of Random Testing (RT) by increasing the diversity of the random test cases throughout the input domain. Many ART algorithms have been investigated such as Fixed-Size-Candidate-Set ART (FSCS) and Restricted Random Testing (RRT), and have been widely used in many practical applications. Despite its popularity, ART suffers from the problem of high computational costs during test-case generation, especially as the number of test cases increases. Although several strategies have been proposed to enhance the ART testing efficiency, such as the forgetting strategy and the k-dimensional tree strategy, these algorithms still face some challenges, including: (1) Although these algorithms can reduce the computation time, their execution costs are still very high, especially when the number of test cases is large; and (2) To achieve low computational costs, they may sacrifice some fault-detection capability. In this paper, we propose an approach based on Approximate Nearest Neighbors (ANNs), called Locality-Sensitive Hashing ART (LSH-ART). When calculating distances among different test inputs, LSH-ART identifies the approximate (not necessarily exact) nearest neighbors for candidates in an efficient way. LSH-ART attempts to balance ART testing effectiveness and efficiency.

cs.SE↗