Software defect prediction based on association rule classification.

In software defect prediction, predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task. However, association based classification methods have not been investigated so far in this context. This paper assesses the use of such a classification method, CBA2, and compares it to other rule based classification methods. Furthermore, we investigate whether rule sets generated on data from one software project can be used to predict defective software modules in other, similar software projects. It is found that applying the CBA2 algorithm results in both accurate and comprehensible rule sets.Software defect prediction; Association rule classification; CBA2; AUC;

Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study

Software defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data Program) datasets. The datasets were classified using k-nearest neighbors (k-NN), decision trees, logistic regression, linear discriminant analysis (LDA), single hidden layer multilayer perceptron (SHL-MLP), and Support Vector Machine (SVM). The hyperparameters of the models were fine-tuned using random search, and the feature dimensionality was decreased by utilizing principal component analysis (PCA). The synthetic minority oversampling technique (SMOTE) was implemented to oversample the minority class in order to correct the class imbalance. k-NN was found to be the most suitable for software defect prediction on several datasets, while SHL-MLP and SVM were also effective on certain datasets. It is noteworthy that logistic regression and LDA did not perform as well as the other models. Moreover, the optimized models outperform the baseline models in terms of classification accuracy. The choice of model for software defect prediction should be based on the specific characteristics of the dataset. Furthermore, hyperparameter tuning can improve the accuracy of machine learning models in predicting software defects

Doctor of Philosophy

dissertationUrinary tract infections (UTIs) afflict millions of individuals yearly, constituting a tremendous global health-care burden. The primary causative agents of UTIs are the gram-negative, rod-shaped bacteria, uropathogenic Escherichia coli (UPEC). These pathogens are motile and adhesive, with a proclivity to colonize diverse niches within the urinary tract; including the kidneys, bladder, and ureters. In the bladder, UPEC grow to high levels and often associate with the superficial epithelial cells lining the lumen. UPEC can invade these superficial epithelial cells to form intracellular reservoir populations, which are thought to be a source of recurrent, or relapsing, infections. The susceptibility of these intracellular UPEC populations was tested using a panel of commonly prescribed antibiotics in a murine model of UTI. Intracellular UPEC were found to persist despite treatment with host cell-permeable antibiotics such as sparfloxacin and ciprofloxacin that effectively sterilize the urine. In a follow-up study, UPEC reservoir populations were more effectively targeted by treating infected bladders with chitosan, a chitin-based bladder exfoliant, prior to sparfloxacin treatment. Although chitosan administration prior to antibiotic treatment significantly decreased UPEC titers, mice still exhibited some relapsing UTIs, suggesting that reservoirs still persist either within the bladder or in other host tissue. To further elucidate mechanisms of bacterial persistence within the urinary tract, several underappreciated bacterial factors were examined that were hypothesized to affect UPEC virulence, stress resistance, and persistence. iv Bacterial, small, non-coding RNAs (sRNAs) are posttranscriptional regulators of gene expression in most prokaryotes and were shown to contribute to a wide variety of UPEC stress response and virulence cascades. In a follow-up study, the putative UPEC sRNA repertoire was defined using RNA-Seq technologies and bioinformatic analyses. Several novel, candidate sRNA molecules were identified and characterized, one of which seemingly repressed UPEC virulence in the murine UTI model. In a second approach to define regulators of UPEC pathogenic behaviors, the tRNA modifying enzyme MiaA was identified as a global regulator of UPEC stress response and virulence. MiaA adds a prenyl group to A-37, adjacent to the anticodon, in a subset of tRNAs to modulate ribosome fidelity and frameshifting. MiaA expression in UPEC was responsive to several environmental stresses and deletion or overexpression of MiaA interferes with the stress resistance and virulence properties of UPEC. Taken together, this thesis defines the robust nature and resilience of intracellular UPEC reservoir populations and delineates sRNAs and MiaA as important regulators of stress resistance and persistence within the host

An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles

The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects

Planning Inspection of Sewer Pipelines Using Defect Based Risk Approach

Due to the poor conditions of wastewater networks, there is an increasing need in the capital investments allocated for enhancing their condition. As per the Canadian Infrastructures Report Card, one third of the total lengths of sewer pipes in Canada is in fair to very poor condition (Canadian Infrastructures Report Card, 2016). As such, there is an urgent need for inspection planning tools, with which decision makers could assess the condition of pipelines and identify pipes with higher risk of failure. These tools are potentially of service in prioritizing and optimizing inspection activities that lead to decisions regarding appropriate courses of action, especially in cases of limited resources and funding. The goal of this research is to develop an optimization model for scheduling the inspection of sewer pipelines by performing defect-based risk assessment. The risk of failure is determined to identify critical pipe sections; by combining likelihood and consequence of failure values using the Sugeno Fuzzy Inference System. The developed optimization model determines the inspection sequence of pipeline sections in addition to optimizing the utilization of inspection crews by minimizing both time and cost of inspections. The risk assessment model is divided into two sub models: likelihood and consequences of failure. Structural and operational defects and pipeline characteristics in an existing sewage network are used to develop the likelihood model that determines the structural, operational and overall condition ratings of pipelines. Method-wise, Bayesian Belief Network (BBN) is used to develop a static condition assessment model using probabilities of occurrences and conditional probabilities. Moreover, time dimension is introduced to the developed BBN model using logistic regression as temporal links which are required to convert BBN into Dynamic Bayesian Network (DBN). The accuracy of the model’s prediction is examined through referencing of actual data, where the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for the BBN model are 0.67, 1.06, 0.56 and 1.05, 1.60, 0.95 for structural, operational and overall conditions, respectively. The second sub-model representing the consequences of failure is developed to determine the impact of sewer pipelines’ failure using Cost Benefit Analysis (CBA). Developing this sub model involves identifying and analyzing costs of failure and benefits resulting from avoiding such failures. In order to validate the CBA model, actual costs from a real failure incident are compared with the proposed model's outputs. During the implementation of the CBA model, it is found that the indirect costs resulting from sewer pipelines’ failure represent a significant portion of the total failure costs. The proposed risk assessment model is validated using actual data derived from inspected sewer pipelines. Cost savings of around 67% could be achieved if the risk assessment model is applied and deployed over ongoing inspection practices followed by municipalities. A Mixed Integer Linear Programming (MILP) model is developed to optimize scheduling of inspection activities by including sewer sections, time and cost of inspections. This model is developed using GAMS and solved using CPLEX to maximize the number of sections and minimize time and cost. The output from the MILP model is compared to the results of another model solved using the Genetic Algorithm (GA) approach. It is found that the MILP model could perform better than the GA model in terms of optimal solutions. Additionally, a resulting inspection cost reduction of approximately 38% could be achieved when utilizing the MILP model. It is expected that the proposed inspection scheduling model could help decision makers better assess the condition of sewer pipelines and improve their decision-making on proactive or reactive measures. The proposed model could help allocate budgets more efficiently in addition, to being an enabler for better inspection programs, particularly in cases of limited funds and task forces

An Adaptive Neuro-Fuzzy Inference System-Based Approach for Oil and Gas Pipeline Defect Depth Estimation

Abstract-To determine the severity of metal-loss defects in oil and gas pipelines, the depth of potential defects, along with their length, needs first to be estimated. For this purpose, pipeline engineers use intelligent Magnetic Flux Leakage (MFL) sensors that scan the metal pipelines and collect defect-related data. However, due to the huge amount of the collected MFL data, the defect depth estimation task is cumbersome, timeconsuming, and error-prone. In this paper, we propose an adaptive neuro-fuzzy inference system (ANFIS)-based approach to estimate defect depths from MFL signals. Depth-related features are first extracted from the MFL signals and then are used to train the neural network to tune the parameters of the membership functions of the fuzzy inference system. A hybrid learning algorithm that combines least-squares and back propagation gradient descent method is adopted. Moreover, to achieve an optimal performance by the proposed approach, highly-discriminant features are selected from the obtained features by using the weight-based support vector machine (SVM). Experimental work has shown that encouraging results are obtained. Within error-tolerance ranges of ±15%, ±20%, ±25%, and ±30%, the depth estimation accuracies obtained by the proposed technique are 80.39%, 87.75%, 91.18%, and 95.59%, respectively. Moreover, further improvement can be easily achieved by incorporating new and more discriminant features

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Security Analysis and Improvement Model for Web-based Applications

Today the web has become a major conduit for information. As the World Wide Web?s popularity continues to increase, information security on the web has become an increasing concern. Web information security is related to availability, confidentiality, and data integrity. According to the reports from http://www.securityfocus.com in May 2006, operating systems account for 9% vulnerability, web-based software systems account for 61% vulnerability, and other applications account for 30% vulnerability. In this dissertation, I present a security analysis model using the Markov Process Model. Risk analysis is conducted using fuzzy logic method and information entropy theory. In a web-based application system, security risk is most related to the current states in software systems and hardware systems, and independent of web application system states in the past. Therefore, the web-based applications can be approximately modeled by the Markov Process Model. The web-based applications can be conceptually expressed in the discrete states of (web_client_good; web_server_good, web_server_vulnerable, web_server_attacked, web_server_security_failed; database_server_good, database_server_vulnerable, database_server_attacked, database_server_security_failed) as state space in the Markov Chain. The vulnerable behavior and system response in the web-based applications are analyzed in this dissertation. The analyses focus on functional availability-related aspects: the probability of reaching a particular security failed state and the mean time to the security failure of a system. Vulnerability risk index is classified in three levels as an indicator of the level of security (low level, high level, and failed level). An illustrative application example is provided. As the second objective of this dissertation, I propose a security improvement model for the web-based applications using the GeoIP services in the formal methods. In the security improvement model, web access is authenticated in role-based access control using user logins, remote IP addresses, and physical locations as subject credentials to combine with the requested objects and privilege modes. Access control algorithms are developed for subjects, objects, and access privileges. A secure implementation architecture is presented. In summary, the dissertation has developed security analysis and improvement model for the web-based application. Future work will address Markov Process Model validation when security data collection becomes easy. Security improvement model will be evaluated in performance aspect