https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4580 2026-10-19T09:28:15Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4963 Title: Visualizing Security Vulnerability Evolution of Software Systems Authors: Sinhabahu, N.N. Abstract: Abstract The analysis of large-scale software and finding security vulnerabilities while its evolving is difficult without using supplementary tools, because of the size and complexity of today’s systems. However, just looking at a report may not transmit the overall picture of the system in terms of security vulnerabilities and its evolution throughout the project lifecycle. Software visualization is a program comprehension technique used in the context of the present and explores large amounts of information precisely. For the analysis of security vulnerabilities of complex software systems, Secure Codecity with evolution is an interactive 3D visualization tool that can be utilized. It utilize techniques and methods that are used in graphical visualualization to illustrating security aspects and the evolution of software. The main goal of the proposed framework defined as uplift, simplify, and clarify the mental representation that a software engineer has a software system and its evolution in terms of its security. Static code was visualized based on a city metaphor, which represents classes as buildings and packages as districts of a city. Identified Vulnerabilities were represented in a different color according to the severity. To visualize different aspects, large variety of options were given. Users can evaluate the evolution of the security vulnerabilities of a system on several versions using matrices provided which will help users go get an overall understanding of security vulnerabilities varies with different versions of the software. This framework was implemented using SonarQube for software vulnerability detection and ThreeJs for implementing the City Metaphor. The evaluation results evidently show that our framework surpasses the existing tools in terms of accuracy, efficiency, and usability. Keywords : 3D software visualization, Vulnerability Evolution, Re-engineering ,Vulnerability Analysis, 3D graphics, human-computer interaction. 2020-01-01T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4962 Title: A Multi-Class Classification Approach for Native Language Identification of English Writers using Machine Learning and Natural Language Processing Techniques Authors: Perera, D. N. Abstract: Abstract This research addresses a multi-class text classification problem using supervised learning approaches. The main focus of this research is to identify the native language of English authors using their written English text using machine learning (ML) and natural language processing (NLP) techniques. According to the literature for text classification most promising ML algorithms are Support Vector Machines (SVMs), Naïve Bayes, and Decision Trees. The main data corpora used for this research were Italki corpus and ICE corpus. Each corpus was divided into two; 70% of the data (seen data) was taken for model building and training and 30% of the data (unseen data) for testing and validation. Data were pre-processed to remove URLs, XML tags, multiple spaces, e-mail addresses, and censored data, etc. Testing have been conducted using Linear Support Vector Machines (LSVM) with Stochastic gradient descent (SGD), Multinomial Naïve Bayes (MNB), Decision Tree (DT) and Ensemble approach. 5- fold cross validation techniques have been used on training data to fine-tune the hyperparameters of the classifier. Further, learning curves were drawn to see whether the ML model is overfitting. Then the ML model has been tested using the unseen data. Three approaches were trained and tested, namely; flat classification, hierarchical classification and sub-category classification. Hierarchical classification received much higher performance when compared to flat classification approach. For the flat classification approach 81.80% accuracy for the ICE corpus and 42.50% for the Italki corpus were received. For the hierarchical classification approach 50.85% accuracy was achieved for the Italki corpus and 63.52% and 68.93% achieved for the Kachru’s model and the geographical distribution model respectively. Subcategorical classification approach achieved highest accuracy of 82.32% for the Non-Native Non Indo European Austronesian category in Italki corpus and 100% accuracy for the Expanded circle category in ICE corpus. When analysing the performance of each machine learning technique LSVM-SGD and MNB have given promising results when compared to DT and RF. In addition, significant features of sub-categories and specially for Sinhala in Italki corpus and Sri Lanka in ICE corpus have been derived and analysed. 2020-11-20T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4961 Title: Deficiency Identification of Greenhouse Lettuce using Explainable AI Authors: Rodrigo, A.R.S.P. Abstract: ABSTRACT The Convolutional Neural Network (CNN) based solutions are used to identify the nutrient deficiencies of the crops based on the color variances of the leaves. However, one of the major problems in the CNN based solutions are the lack of ability to explain the results obtained. This research is focused on overcoming this challenge by combining the results obtained from CNN with TensorFlow Inference Engine to provide humanely understandable results for deficiency identification of crops. Therefore, greenhouse lettuce is selected as the crop for the study. Greenhouse farming became popular with the technological evolution in the last few decades. This aims to provide an optimal nutrient composition to the corps, to protect the crops from pests without applying pesticides, and to provide the optimal environmental conditions to the corps such as temperature, humidity, etc. Lettuce is one of the mostly consumed vegetable crops among the green house crops but facing a yield loss due the nutrient deficiencies. Therefore, the early identification of deficiencies becomes crucial. A custom test bed is created to gather data/images and using those data/images YOLOv3 object detection model was trained to detect Calcium, Nitrogen, and Magnesium nutrient deficiencies of greenhouse lettuce. The results demonstrate a mean average precision of 94.38% on training data and 75.53% on custom data. The trained weights were combined with the TensorFlow Inference Engine to provide explainable results using a local knowledge base of deficiencies 2020-11-14T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4960 Title: Optimizing costs of Serverless: Function profiling approach to optimize memory and running time of Serverless functions Authors: Munasinghe, A. K. D. M. P. Abstract: Abstract Function profiling is a mechanism used by developers, performance analysts and architects to evaluate resource usages of a particular function. When coding was done locally and deployed in local or in-house servers profiling was used as a method to evaluate the performance of a program. With the emergence of Serverless the function deployment environment no longer reside on a local Server. The runtime is not accessible for the developer and is maintained by an external entity. With a minimal availability of system information developers are using brute force techniques to identify the required memory parameter for function deployment. The public unavailability of the underlying CPU allocation which correlates to the provided memory parameter intensifies the requirement for the use of brute force methods. This study presents a Proof of concept and an experimental study to define how function profiling can be used to provide a combined memory parameter out of CPU, memory and running time produced by a profiler. The resulting output is then mapped against a memory configuration for function deployment in a Serverless environment. In the two fold research methodology selection of profilers and the platforms are carried out as proof of concept. Then the proof of concept is converted to an experimental study to deep dive into Serverless platform to observe the behavior of Serverless memory and CPU. Finally, by utilizing regression models a fine-tuned cost estimation function is defined. The study successfully updated the knowledge over mapping local CPU time to Serverless CPU time based on the memory parameter with a linear regression model. This further optimizes the running time of a function which is expandable in a range of CPU parameters and Memory configurations. With the understanding of the underlying environments, functions for estimating Serverless function. The profiler techniques, CPU time-sharing and Serverless cost estimation are the primary outputs of the research. Which is then backed with an algorithm definition to develop an analysis tool to achieve cost optimizations in a Serverless platform using function profiler "memory_profiler" and scalene for Python. 2020-01-01T00:00:00Z