Transfer Learning Techniques for Domain-Specific Quality Assurance Testing

Abstract

QA professionals like AI because it produces software rapidly and reliably. Traditional software testing increases costs and delays product introduction due to time and resource restrictions. Transfer learning lets pre-trained AI models evaluate domain-specific tasks. This boosts software and product launches. This study examines domain-specific quality assurance testing transfer learning theoretically and practically. It emphasises how different methods may speed up testing, use resources better, and cover more tests. 

Transfer learning lets robots use knowledge from similar but distinct fields. Customise general-purpose dataset models for specific software environments or use cases using software testing. With pre-trained AI models for each testing assignment, companies save time and effort. This strategy generalises AI systems across software domains to lower model generation costs and enhance testing. Quality assurance teams may use transfer learning to detect issues, build test cases, and assess performance. 

Domain-specific QA annotation may benefit from transfer learning. Labelling big, specialist datasets takes time and manpower. With some new tagged data, transfer learning adjusts pre-trained models to the target domain after huge data training. Faster training and model release minimise testing cycles without sacrificing outcomes. 

We also highlight transfer learning's software QA restrictions. Similar source and target domains aid transfer learning. If the source and destination domains don't match, the transferred model may need target domain fine-tuning or new models. With antiquated systems and technology, QA may have trouble testing transfer learning models. These difficulties show the need of a robust basis for transfer learning software testing. 

Also vital for software testing is transfer learning. Domain adaptation, fine-tuning, and feature extraction are used. Each has quality assurance pros and cons. Fine-tuned pre-trained model weights match domain. Feature extraction employs pre-trained model learning features without restructuring. Data transmission requires domain adaptation to match source and destination domains. This study explains how these tactics may improve quality assurance.
Transfer learning-based domain-specific QA testing case studies are also offered. These cases demonstrate how transfer learning aids banking, healthcare, and software testing. Transfer learning always lowered testing time, detected more issues, and increased product dependability. These examples demonstrate how quality assurance professionals and organisations employ transfer learning. 

The paper recommends further transfer learning research in software testing notwithstanding these encouraging case studies. Future research should improve scalability, performance in changing software environments, and tool automation to assess transfer learning models. AI testing raises fairness and data privacy problems, the report claims. If AI-based testing becomes popular, consider it.