Software defect prediction framework based on hybrid metaheuristic optimization methods

A software defect is an error, failure, or fault in a software that produces an incorrect or unexpected result. Software defects are expensive in quality and cost. The accurate prediction of defect‐prone software modules certainly assist testing effort, reduce costs and improve the quality of software. The classification algorithm is a popular machine learning approach for software defect prediction. Unfortunately, software defect prediction remains a largely unsolved problem. As the first problem, the comparison and benchmarking results of the defect prediction using machine learning classifiers indicate that, the poor accuracy level is dominant and no particular classifiers perform best for all the datasets. There are two main problems that affect classification performance in software defect prediction: noisy attributes and imbalanced class distribution of datasets, and difficulty of selecting optimal parameters of the classifiers. In this study, a software defect prediction framework that combines metaheuristic optimization methods for feature selection and parameter optimization, with meta learning methods for solving imbalanced class problem on datasets, which aims to improve the accuracy of classification models has been proposed. The proposed framework and models that are are considered to be the specific research contributions of this thesis are: 1) a comparison framework of classification models for software defect prediction known as CF-SDP, 2) a hybrid genetic algorithm based feature selection and bagging technique for software defect prediction known as GAFS+B, 3) a hybrid particle swarm optimization based feature selection and bagging technique for software defect prediction known as PSOFS+B, and 4) a hybrid genetic algorithm based neural network parameter optimization and bagging technique for software defect prediction, known as NN-GAPO+B. For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. The proposed framework and methods are evaluated using the state-of-the-art datasets from the NASA metric data repository. The results indicated that the proposed methods (GAFS+B, PSOFS+B and NN-GAPO+B) makes an impressive improvement in the performance of software defect prediction. GAFS+B and PSOFS+B significantly affected on the performance of the class imbalance suffered classifiers, such as C4.5 and CART. GAFS+B and PSOFS+B also outperformed the existing software defect prediction frameworks in most datasets. Based on the conducted experiments, logistic regression performs best in most of the NASA MDP datasets, without or with feature selection method. The proposed methods also generated the selected relevant features in software defect prediction. The top ten most relevant features in software defect prediction include branch count metrics, decision density, halstead level metric of a module, number of operands contained in a module, maintenance severity, number of blank LOC, halstead volume, number of unique operands contained in a module, total number of LOC and design density

Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

2010-2011 > Academic research: refereed > Chapter in an edited book (author

Predicting Software Reliability Using Ant Colony Optimization Technique with Travelling Salesman Problem for Software Process – A Literature Survey

Computer software has become an essential and important foundation in several versatile domains including medicine, engineering, etc. Consequently, with such widespread application of software, there is a need of ensuring software reliability and quality. In order to measure such software reliability and quality, one must wait until the software is implemented, tested and put for usage for a certain time period. Several software metrics have been proposed in the literature to avoid this lengthy and costly process, and they proved to be a good means of estimating software reliability. For this purpose, software reliability prediction models are built. Software reliability is one of the important software quality features. Software reliability is defined as the probability with which the software will operate without any failure for a specific period of time in a specified environment. Software reliability, when estimated in early phases of software development life cycle, saves lot of money and time as it prevents spending huge amount of money on fixing of defects in the software after it has been deployed to the client. Software reliability prediction is very challenging in starting phases of life cycle model. Software reliability estimation has thus become an important research area as every organization aims to produce reliable software, with good quality and error or defect free software. There are many software reliability growth models that are used to assess or predict the reliability of the software. These models help in developing robust and fault tolerant systems. In the past few years many software reliability models have been proposed for assessing reliability of software but developing accurate reliability prediction models is difficult due to the recurrent or frequent changes in data in the domain of software engineering. As a result, the software reliability prediction models built on one dataset show a significant decrease in their accuracy when they are used with new data. The main aim of this paper is to introduce a new approach that optimizes the accuracy of software reliability predictive models when used with raw data. Ant Colony Optimization Technique (ACOT) is proposed to predict software reliability based on data collected from literature. An ant colony system by combining with Travelling Sales Problem (TSP) algorithm has been used, which has been changed by implementing different algorithms and extra functionality, in an attempt to achieve better software reliability results with new data for software process. The intellectual behavior of the ant colony framework by means of a colony of cooperating artificial ants are resulting in very promising results. Keywords: Software Reliability, Reliability predictive Models, Bio-inspired Computing, Ant Colony Optimization technique, Ant Colon

Global DNA methylation and transcriptional analyses of human ESC-derived cardiomyocytes.

With defined culture protocol, human embryonic stem cells (hESCs) are able to generate cardiomyocytes in vitro, therefore providing a great model for human heart development, and holding great potential for cardiac disease therapies. In this study, we successfully generated a highly pure population of human cardiomyocytes (hCMs) (>95% cTnT(+)) from hESC line, which enabled us to identify and characterize an hCM-specific signature, at both the gene expression and DNA methylation levels. Gene functional association network and gene-disease network analyses of these hCM-enriched genes provide new insights into the mechanisms of hCM transcriptional regulation, and stand as an informative and rich resource for investigating cardiac gene functions and disease mechanisms. Moreover, we show that cardiac-structural genes and cardiac-transcription factors have distinct epigenetic mechanisms to regulate their gene expression, providing a better understanding of how the epigenetic machinery coordinates to regulate gene expression in different cell types

Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected

Pembangunan model penentuan keperluan perumahan kajian kes: Johor Bahru, Malaysia

Perumahan merupakan satu komponen penting dalam pembangunan ekonomi di mana ia telah menjadi dasar kerajaan untuk menyediakan rumah bagi setiap rakyat. Rancangan Malaysia terdahulu telah cuba merancang bagi merealisasikan dasar ini. Walaupun anggaran keperluan perumahan dibuat di bawah Rancangan Malaysia, namun anggaran tersebut tidak membayangkan keperluan sebenar pembeli dan penyewa rumah di Malaysia. Negara-negara maju telah menggunakan pelbagai model dalam menentukan keperluan perumahan. Namun begitu, model-model tersebut tidak sesuai digunakan di Malaysia kerana data yang terhad. Kajian ini memfokuskan kepada dua objektif iaitu, mengenal pasti model dan faktor yang signifikan bagi menentukan keperluan perumahan, dan kedua menghasilkan model penentuan keperluan perumahan di Malaysia. Skop kajian ini tertumpu kepada pembeli dan penyewa rumah di Daerah Johor Bahru yang dipilih melalui kaedah pesampelan kelompok pelbagai peringkat. Data diperolehi melalui borang kaji selidik dan dianalisis menggunakan pendekatan kuantitatif. Analisis statistik deskriptif digunakan bagi menghuraikan taburan kekerapan, peratus, min, dan sisihan piawai manakala statistik inferensi iaitu ujian Korelasi Pearson dan Regresi Pelbagai digunakan untuk pembentukan model. Dengan menggunakan kaedah Enter, satu model yang signifikan dapat dihasilkan (F4,178 = 353.699 p < 0.05. Adjusted R square = .886) yang signifikan terhadap dua faktor utama iaitu demografi dan kemampuan. Model yang dihasilkan bagi kajian ini adalah General Linear Model. Model ini dapat digunakan bagi menentukan keperluan perumahan di Johor Bahru. Ia juga berfungsi sebagai alat penting dalam perancangan sektor perumahan pada masa hadapan di Malaysia

Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy

It is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured parts. Also, the energy density is insufficient to detect the process window for producing full dense components. In fact, parts produced with the same energy density but different combinations of parameters may present different properties even under the microstructural viewpoint. In this context, the need to assess the influence of the process parameters and to select the best parameters set able to optimize the final properties of SLM parts has been capturing the attention of both academics and practitioners. In this paper different hybrid prediction-optimization approaches for maximizing the relative density of Ti6Al4V SLM manufactured parts are proposed. An extended design of experiments involving six process parameters has been configured for constructing two surrogate models based on response surface methodology (RSM) and artificial neural network (ANN), respectively. The optimization phase has been performed by means of evolutionary computations. To this end, three nature-inspired metaheuristic algorithms have been integrated with the prediction modelling structures. A series of experimental tests has been carried out to validate the results from the proposed hybrid optimization procedures. Also, a sensitivity analysis based on the results from the analysis of variance was executed to evaluate the influence of the processing parameter and their reciprocal interactions on the part porosity