Analyze the Performance of Software by Machine Learning Methods for Fault Prediction Techniques

Trend of using the software in daily life is increasing day by day. Software system development is growing more difficult as these technologies are integrated into daily life. Therefore, creating highly effective software is a significant difficulty. The quality of any software system continues to be the most important element among all the required characteristics. Nearly one-third of the total cost of software development goes toward testing. Therefore, it is always advantageous to find a software bug early in the software development process because if it is not found early, it will drive up the cost of the software development. This type of issue is intended to be resolved via software fault prediction. There is always a need for a better and enhanced prediction model in order to forecast the fault before the real testing and so reduce the flaws in the time and expense of software projects. The various machine learning techniques for classifying software bugs are discussed in this paper

A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

Predicting when and where bugs will appear in software may assist improve quality and save on software testing expenses. Predicting bugs in individual modules of software by utilizing machine learning methods. There are, however, two major problems with the software defect prediction dataset: Social stratification (there are many fewer faulty modules than non-defective ones), and noisy characteristics (a result of irrelevant features) that make accurate predictions difficult. The performance of the machine learning model will suffer greatly if these two issues arise. Overfitting will occur, and biassed classification findings will be the end consequence. In this research, we suggest using machine learning approaches to enhance the usefulness of the CatBoost and Gradient Boost classifiers while predicting software flaws. Both the Random Over Sampler and Mutual info classification methods address the class imbalance and feature selection issues inherent in software fault prediction. Eleven datasets from NASA's data repository, "Promise," were utilised in this study. Using 10-fold cross-validation, we classified these 11 datasets and found that our suggested technique outperformed the baseline by a significant margin. The proposed methods have been evaluated based on their abilities to anticipate software defects using the most important indices available: Accuracy, Precision, Recall, F1 score, ROC values, RMSE, MSE, and MAE parameters. For all 11 datasets evaluated, the suggested methods outperform baseline classifiers by a significant margin. We tested our model to other methods of flaw identification and found that it outperformed them all. The computational detection rate of the suggested model is higher than that of conventional models, as shown by the experiments.

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

Litigating Partial Autonomy

Who is responsible when a semi-autonomous vehicle crashes? Automobile manufacturers claim that because Advanced Driver Assistance Systems (ADAS) require constant human oversight even when autonomous features are active, the driver is always fully responsible when supervised autonomy fails. This Article argues that the automakers’ position is likely wrong both descriptively and normatively. On the descriptive side, current products liability law offers a pathway toward shared legal responsibility. Automakers, after all, have engaged in numerous marketing efforts to gain public trust in automation features. When drivers’ trust turns out to be misplaced, drivers are not always able to react in a timely fashion to re-take control of the car. In such cases, the automaker is likely to face primary liability, perhaps with a reduction for the driver’s comparative fault. On the normative side, this Article argues that the nature of modern semi-autonomous systems requires the human and machine to engage in a collaborative driving endeavor. The human driver should not bear full liability for the harm arising from this shared responsibility. As lawsuits involving partial autonomy increase, the legal system will face growing challenges in incentivizing safe product development, allocating liability in line with fair principles, and leaving room for a nascent technology to improve in ways that, over time, will add substantial safety protections. The Article develops a framework for considering how those policy goals can play a role in litigation involving autonomous features. It offers three key recommendations, including (1) that courts consider collaborative driving as a system when allocating liability; (2) that the legal system recognize and encourage regular software updates for vehicles, and (3) that customers pursue fraud and warranty claims when manufacturers overstate their autonomous capabilities. Claims for economic damages can encourage manufacturers to internalize the cost of product defects before, rather than after, their customers suffer serious physical injury