One of the most important jobs in the SDLC’s Testing Phase is software defect prediction. It determines which modules are prone to failure and require extensive testing. This allows for efficient use of testing resources while staying within the limits. Predicting software faults is a significant and effective method for increasing software quality and reliability. Modeling which components in a large software system will have the most difficulties in the next release helps project managers better manage projects, such as early detection of potential release delays and cost-effectively guiding remedial actions to improve software quality. Developing robust fault prediction models, on the other hand, is a difficult task, and numerous solutions have been proposed in the literature. In this survey, we look at convolutional neural networks, defect prediction via attention mechanism, and other deep learning techniques that can help detect software defects. Program defect prediction is used to aid developers in spotting probable flaws and prioritizing their testing efforts in order to increase software reliability.

If ambulances are delayed at urban traffic junctions, patient survival chances during medical emergencies can drop. Previous systems used single-modality vehicular detection for both ambulances and traffic police. Communication was similarly limited to a single channel among the various actors, namely, traffic police, ambulance crew, and hospital. This led to missed detections, poor coordination, avoidable delays, and so on. This project aims to design an intelligent system that authenticates ambulances in real time and controls traffic signals via IoT-based technology. It will also provide a platform for simultaneous communication between police, hospitals, and other concerned parties. By fusing a visual detection model with an audio-based siren classifier, the system uses a confidence-based adaptive decision mechanism for reliable detection in challenging scenarios involving occlusion, low light, and high ambient noise. When detection is confirmed, an IoT edge device triggers real-time traffic signal preemption to create a green corridor. This will work without any changes to ambulance hardware. Moreover, a cross-platform mobile application can help ambulance staff and police communicate with hospital staff. It allows manual SOS signals, signal override, patient information sharing, and telemedicine. The system includes an in-ambulance patient monitoring module that detects critical clinical conditions and sends real-time alerts to the hospital before arrival. Based on experimental results, the system achieved 98.4 parcent video accuracy and 97.9 parcent audio accuracy with low latency. The proposed framework improved the efficiency and coordination of emergency response and pre-arrival patient care in urban environments.