Transfer learning for conflict and duplicate detection in software requirement pairs

• URL: http://arxiv.org/abs/2301.03709v2
• Date: Tue, 30 Jul 2024 16:31:46 GMT
• Title: Transfer learning for conflict and duplicate detection in software requirement pairs
• Authors: Garima Malik, Savas Yildirim, Mucahit Cevik, Ayse Bener, Devang Parikh,
• Abstract summary: Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We design a novel transformers-based architecture, SR-BERT, which incorporates Sentence-BERT and Bi-encoders for the conflict and duplicate identification task.
• Score: 0.5359378066251386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We formulate the conflict and duplicate detection problem as a requirement pair classification task. We design a novel transformers-based architecture, SR-BERT, which incorporates Sentence-BERT and Bi-encoders for the conflict and duplicate identification task. Furthermore, we apply supervised multi-stage fine-tuning to the pre-trained transformer models. We test the performance of different transfer models using four different datasets. We find that sequentially trained and fine-tuned transformer models perform well across the datasets with SR-BERT achieving the best performance for larger datasets. We also explore the cross-domain performance of conflict detection models and adopt a rule-based filtering approach to validate the model classifications. Our analysis indicates that the sentence pair classification approach and the proposed transformer-based natural language processing strategies can contribute significantly to achieving automation in conflict and duplicate detection

Related papers

• Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent [74.02034188307857]
  Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data. We find existing methods inevitably discard task-specific information that, while causing conflicts, is crucial for performance. Our approach consistently outperforms previous methods, achieving state-of-the-art results across diverse architectures and tasks in both vision and NLP domains.
  arXiv  Detail & Related papers  (2025-01-02T12:45:21Z)
• PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification [5.463986763897077]
  Early detection and resolution of duplicate and conflicting requirements can significantly enhance project efficiency and overall software quality. Researchers have developed various computational predictors by leveraging Artificial Intelligence (AI) potential to detect duplicate and conflicting requirements. This research offers a comprehensive framework that facilitate development of 3 different types of predictive pipelines.
  arXiv  Detail & Related papers  (2024-12-02T16:05:38Z)
• Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection [6.153407718616422]
  Deep learning methods have become integral to embedding behavior sequence data in fraud detection. We introduce the multitask CNN behavioral Embedding Model for Transaction Fraud Detection. Our contributions include 1) introducing a single-layer CNN design featuring multirange kernels which outperform LSTM and Transformer models in terms of scalability and domain-focused inductive bias.
  arXiv  Detail & Related papers  (2024-11-29T03:58:11Z)
• Identifying Technical Debt and Its Types Across Diverse Software Projects Issues [4.6173290119212265]
  Technical Debt (TD) identification in software projects issues is crucial for maintaining code quality, reducing long-term maintenance costs, and improving overall project health. This study advances TD classification using transformer-based models, addressing the critical need for accurate and efficient TD identification in large-scale software development.
  arXiv  Detail & Related papers  (2024-08-17T07:46:54Z)
• Single-Stage Visual Relationship Learning using Conditional Queries [60.90880759475021]
  TraCQ is a new formulation for scene graph generation that avoids the multi-task learning problem and the entity pair distribution. We employ a DETR-based encoder-decoder conditional queries to significantly reduce the entity label space as well. Experimental results show that TraCQ not only outperforms existing single-stage scene graph generation methods, it also beats many state-of-the-art two-stage methods on the Visual Genome dataset.
  arXiv  Detail & Related papers  (2023-06-09T06:02:01Z)
• Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models [80.23791222509644]
  Inconsistent AI models are considered brittle and untrustworthy by human users. We find that state-of-the-art vision-language models suffer from a surprisingly high degree of inconsistent behavior across tasks. We propose a rank correlation-based auxiliary training objective, computed over large automatically created cross-task contrast sets.
  arXiv  Detail & Related papers  (2023-03-28T16:57:12Z)
• Transformer-based approaches to Sentiment Detection [55.41644538483948]
  We examined the performance of four different types of state-of-the-art transformer models for text classification. The RoBERTa transformer model performs best on the test dataset with a score of 82.6% and is highly recommended for quality predictions.
  arXiv  Detail & Related papers  (2023-03-13T17:12:03Z)
• Transformers for End-to-End InfoSec Tasks: A Feasibility Study [6.847381178288385]
  We implement transformer models for two distinct InfoSec data formats - specifically URLs and PE files. We show that our URL transformer model requires a different training approach to reach high performance levels. We demonstrate that this approach performs comparably to well-established malware detection models on benchmark PE file datasets.
  arXiv  Detail & Related papers  (2022-12-05T23:50:46Z)
• Paragraph-based Transformer Pre-training for Multi-Sentence Inference [99.59693674455582]
  We show that popular pre-trained transformers perform poorly when used for fine-tuning on multi-candidate inference tasks. We then propose a new pre-training objective that models the paragraph-level semantics across multiple input sentences.
  arXiv  Detail & Related papers  (2022-05-02T21:41:14Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims [3.7543966923106438]
  We introduce the first adversarially-regularized, transformer-based claim spotter model. We obtain a 4.70 point F1-score improvement over current state-of-the-art models. We propose a method to apply adversarial training to transformer models.
  arXiv  Detail & Related papers  (2020-02-18T16:51:05Z)

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.