Semantic Constraint Inference for Web Form Test Generation

• URL: http://arxiv.org/abs/2402.00950v2
• Date: Mon, 22 Jul 2024 21:58:51 GMT
• Title: Semantic Constraint Inference for Web Form Test Generation
• Authors: Parsa Alian, Noor Nashid, Mobina Shahbandeh, Ali Mesbah,
• Abstract summary: We introduce an innovative approach, called FormNexus, for automated web form test generation. FormNexus emphasizes deriving semantic insights from individual form elements and relations among them. We show that FormNexus combined with GPT-4 achieves 89% coverage in form submission states.
• Score: 6.0759036120654315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated test generation for web forms has been a longstanding challenge, exacerbated by the intrinsic human-centric design of forms and their complex, device-agnostic structures. We introduce an innovative approach, called FormNexus, for automated web form test generation, which emphasizes deriving semantic insights from individual form elements and relations among them, utilizing textual content, DOM tree structures, and visual proximity. The insights gathered are transformed into a new conceptual graph, the Form Entity Relation Graph (FERG), which offers machine-friendly semantic information extraction. Leveraging LLMs, FormNexus adopts a feedback-driven mechanism for generating and refining input constraints based on real-time form submission responses. The culmination of this approach is a robust set of test cases, each produced by methodically invalidating constraints, ensuring comprehensive testing scenarios for web forms. This work bridges the existing gap in automated web form testing by intertwining the capabilities of LLMs with advanced semantic inference methods. Our evaluation demonstrates that FormNexus combined with GPT-4 achieves 89% coverage in form submission states. This outcome significantly outstrips the performance of the best baseline model by a margin of 25%.

Related papers

• WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization [68.46693401421923]
  WebShaper systematically formalizes IS tasks through set theory.<n>WebShaper achieves state-of-the-art performance among open-sourced IS agents on GAIA and WebWalkerQA benchmarks.
  arXiv  Detail & Related papers  (2025-07-20T17:53:37Z)
• Automated Web Application Testing: End-to-End Test Case Generation with Large Language Models and Screen Transition Graphs [0.5965410190046627]
  This paper presents an automated system for generating test cases for two key aspects of web application testing: site navigation and form filling.<n>For site navigation, the system employs screen transition graphs and LLMs to model navigation flows and generate test scenarios.<n>For form filling, it uses state graphs to handle conditional forms and automates Selenium script generation.
  arXiv  Detail & Related papers  (2025-06-03T07:08:21Z)
• FormFactory: An Interactive Benchmarking Suite for Multimodal Form-Filling Agents [36.11725924594441]
  Current online form filling tools are largely rule-based and lack generalizable, generative capabilities.<n>We propose FormFactory, an interactive benchmarking suite comprising a web-based interface, backend evaluation module, and dataset.<n>Our benchmark covers diverse real-world scenarios, incorporates various field formats, and simulates high-fidelity form interactions.
  arXiv  Detail & Related papers  (2025-06-02T10:34:57Z)
• DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation [0.13124513975412253]
  We present a novel framework for testing vision neural networks that leverages Large Language Models and control-conditioned Diffusion Models. Our approach begins by translating images into detailed textual descriptions using a captioning model. These descriptions are then used to produce new test images through a text-to-image diffusion process.
  arXiv  Detail & Related papers  (2025-02-05T16:35:42Z)
• DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning [7.036629164442979]
  We introduce the DocTTT framework to address these challenges. Key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder(MAE)
  arXiv  Detail & Related papers  (2025-01-22T14:18:47Z)
• An efficient approach to represent enterprise web application structure using Large Language Model in the service of Intelligent Quality Engineering [0.0]
  This paper presents a novel approach to represent enterprise web application structures using Large Language Models (LLMs) We introduce a hierarchical representation methodology that optimize the few-shot learning capabilities of LLMs. Our methodology addresses existing challenges around usage of Generative AI techniques in automated software testing.
  arXiv  Detail & Related papers  (2025-01-12T15:10:57Z)
• Automatic Generation of Behavioral Test Cases For Natural Language Processing Using Clustering and Prompting [6.938766764201549]
  This paper introduces an automated approach to develop test cases by exploiting the power of large language models and statistical techniques. We analyze the behavioral test profiles across four different classification algorithms and discuss the limitations and strengths of those models.
  arXiv  Detail & Related papers  (2024-07-31T21:12:21Z)
• XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser [35.69888780388425]
  In this work, we introduce a simple but effective textbfMultimodal and textbfMultilingual semi-structured textbfFORM textbfXForm framework. textbfXForm is anchored on a comprehensive pre-trained language model and innovatively amalgamates entity recognition and relationRE. Our framework exhibits exceptionally improved performance across tasks in both multi-language and zero-shot contexts.
  arXiv  Detail & Related papers  (2024-05-27T16:37:17Z)
• CELA: Cost-Efficient Language Model Alignment for CTR Prediction [71.85120354973073]
  Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs) We propose textbfCost-textbfEfficient textbfLanguage Model textbfAlignment (textbfCELA) for CTR prediction.
  arXiv  Detail & Related papers  (2024-05-17T07:43:25Z)
• Large Language Models for Automated Web-Form-Test Generation: An Empirical Study [8.32635005234879]
  Large Language Models (LLMs) have shown great potential for contextual text generation.<n>No comparative study examining different LLMs has yet been reported for web-form-test generation.<n>We propose three HTML-structure-pruning methods to extract key contextual information.
  arXiv  Detail & Related papers  (2024-05-16T10:21:03Z)
• Automating REST API Postman Test Cases Using LLM [0.0]
  This research paper is dedicated to the exploration and implementation of an automated approach to generate test cases using Large Language Models. The methodology integrates the use of Open AI to enhance the efficiency and effectiveness of test case generation. The model that is developed during the research is trained using manually collected postman test cases or instances for various Rest APIs.
  arXiv  Detail & Related papers  (2024-04-16T15:53:41Z)
• Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search [60.626459715780605]
  Given a descriptive text query, text-based person search aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. In this paper, we propose a simple yet effective dual Transformer model for text-based person search.
  arXiv  Detail & Related papers  (2023-11-15T16:26:49Z)
• Learning-Based Biharmonic Augmentation for Point Cloud Classification [79.13962913099378]
  Biharmonic Augmentation (BA) is a novel and efficient data augmentation technique. BA diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. We present AdvTune, an advanced online augmentation system that integrates adversarial training.
  arXiv  Detail & Related papers  (2023-11-10T14:04:49Z)
• Neural Embeddings for Web Testing [49.66745368789056]
  Existing crawlers rely on app-specific, threshold-based, algorithms to assess state equivalence. We propose WEBEMBED, a novel abstraction function based on neural network embeddings and threshold-free classifiers. Our evaluation on nine web apps shows that WEBEMBED outperforms state-of-the-art techniques by detecting near-duplicates more accurately.
  arXiv  Detail & Related papers  (2023-06-12T19:59:36Z)
• GVdoc: Graph-based Visual Document Classification [17.350393956461783]
  We propose GVdoc, a graph-based document classification model. Our approach generates a document graph based on its layout, and then trains a graph neural network to learn node and graph embeddings. We show that our model, even with fewer parameters, outperforms state-of-the-art models on out-of-distribution data.
  arXiv  Detail & Related papers  (2023-05-26T19:23:20Z)
• Progressive Tree-Structured Prototype Network for End-to-End Image Captioning [74.8547752611337]
  We propose a novel Progressive Tree-Structured prototype Network (dubbed PTSN) PTSN is the first attempt to narrow down the scope of prediction words with appropriate semantics by modeling the hierarchical textual semantics. Our method achieves a new state-of-the-art performance with 144.2% (single model) and 146.5% (ensemble of 4 models) CIDEr scores on Karpathy' split and 141.4% (c5) and 143.9% (c40) CIDEr scores on the official online test server.
  arXiv  Detail & Related papers  (2022-11-17T11:04:00Z)
• Plug and Play Counterfactual Text Generation for Model Robustness [12.517365153658028]
  We introduce CASPer, a plug-and-play counterfactual generation framework. We show that CASPer effectively generates counterfactual text that follow the steering provided by an attribute model. We also show that the generated counterfactuals can be used for augmenting the training data and thereby fixing and making the test model more robust.
  arXiv  Detail & Related papers  (2022-06-21T14:25:21Z)
• Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks [58.0240970093372]
  This paper presents a new framework for human body part segmentation based on Deep Convolutional Neural Networks trained using only synthetic data. The proposed approach achieves cutting-edge results without the need of training the models with real annotated data of human body parts.
  arXiv  Detail & Related papers  (2021-02-02T12:26:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.