Fault Prediction Techniques Research Articles

Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniques

As service-oriented computing systems become more buoyant and complex, the occurrence of faults dramatically increases. Fault prediction plays a crucial role in the service-oriented computing paradigm, aiming to reduce testing cost while maximizing testing quality to utilize testing resources effectively and increase the reliability of service compositions. Although various fault prediction techniques were considered in software testing, service-oriented systems were less fortunate, in which most of the studies have focused on single web services testing rather than service compositions. Moreover, mainly the detection of faulty/non-faulty services was addressed, ignoring the estimate of faults count, their severity, as well as predicting when and where such faults would occur. In this paper, a multilateral model-based fault prediction and localization approach is proposed using deep learning techniques for web service compositions testing rather than single web service testing, which uniquely predicts not only faulty services, but also their count and severity level, location of faults, and time at which faults would occur. Three deep learning models are investigated: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and a proposed hybrid model based on both CNN and RNN. The proposed approach is language-independent, as it adopts process metrics rather than code metrics to overcome the code unavailability concern of services. The experimental analysis adopted main performance metrics on multiple public datasets to evaluate its efficiency and effectiveness. The results indicated that the hybrid CNN_RNN model achieves an average accuracy range of 84%–95.7%, where the RNN and CNN models individually achieve 75%–90% and 70%-79.3% respectively. Thus, the hybrid model increases the accuracy level by 5%–10% and 15%–20%, while achieving the least mean square error of 30% and 60% compared to the RNN and CNN models respectively. In terms of time, the RNN model consumes less average time as of 30–50 ms for the different datasets of variant sizes compared to the CNN and hybrid CNN_RNN models that consume 79–102 and 177–224 ms respectively. Thus, RNN model consumes around 50%–80% less time than those of the CNN and hybrid models respectively.

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.

Explaining software fault predictions to spreadsheet users

A variety of automated software fault prediction techniques was proposed in recent years, in particular for the important class of spreadsheet programs. Software fault prediction techniques commonly create ranked lists of “suspicious” program statements for developers to inspect. Existing research, however, suggests that solely providing such ranked lists may not always be effective. In particular, it was found that developers often seek for explanations for the outcomes provided by a debugging tool and that such explanations may be key for developers to trust and rely on the tool. Research on how to explain the outcomes of fault prediction techniques, which are often based on complex machine learning models, is scarce, and little is known regarding how such explanations are perceived by developers. With this work, we aim to narrow this research gap and study the perception of different forms of explanations by spreadsheet users in the context of a machine learning based fault prediction tool. A between-subjects user study (N=120) revealed significant differences between the explored explanation styles. In particular, we found that well-designed natural language explanations can indeed help users better understand why certain spreadsheet cells were marked by the debugging tool and that such explanations can be effective to increase the users’ trust compared to a black box system.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Artificial Intelligence Enabled Effective Fault Prediction Techniques in Cloud Computing Environment for Improving Resource Optimization

The bulk of the fault tolerance techniques that are in use today laid their primary emphasis, in the event that a virtual machine fails, on the production of clones to replace it, rather than on the early prediction of the failure itself in advance. Several of the currently used techniques give migration priority over recovery in the event that a virtual machine (VM) fails. This is due to resource constraints and concerns with server availability. Examples of algorithms with a single objective include fault tolerance, migration prediction, and simply expecting failure. Another example is fault tolerance. In this research, we are aiming to determine the most effective strategy to transition from a system that is not operating well to one that does. It is essential to be able to predict the failure of a virtual machine in a timely manner due of issues such as squandered resources, energy, and cost. Since the beginning of cloud computing, there has been an issue with the dependability of virtual computers, often known as VMs. As an integral component of a fault tolerance system, preemptive measures are an absolute need in order to guarantee the continuation of service. As a consequence of this, it is vital to work toward enhancing and emphasizing the proactive failure prediction of virtual machines. The key motivations for this are decreased periods of downtime and enhanced scalability. A technique was utilized to transfer the resources that were predicted to fail from one virtual machine (VM) to another VM in a safe manner. Using the compression strategy reduced the amount of time needed to complete the migration, and resource utilization increased. This article provides artificial intelligence that enables effective fault prediction techniques in cloud computing to improve resource optimization.

Comparative Analysis of Machine Learning Technique to Improve Software Defect Prediction

One of the most active areas of research in the software engineering community is defect prediction. The gap between data mining and software engineering must be bridged to increase the rate of software success. Before the testing phase, software defect prediction predicts where these flaws will most likely occur in the source code. Methods for predicting software defects are widely used to investigate the impact area in software using various techniques (clustering, statistical methods, neural networks, and machine learning models). The goal of this research is to examine various machine learning algorithms for predicting software defects. There have been many fault prediction techniques introduced, but no single technique or approach can be used for all types of datasets. To achieve maximum accuracy, different machine learning algorithms such as Bayesian Net, Logistic Regression, Multilayer Perceptron, Ruler Zero-R, J48, Lazy IBK, Support Vector Machine, Neural Networks, Random Forest, and Decision stump were used to uncover the maximum subset of defects that could be predicted. This research concern is to find out defects using five NASA data sets JM1, CM1, KC1, KC2, and PC1. Logistic Regression has been shown to have the best output relative to others (93%).

Classification of Fault Prediction: A Mapping Study

Software fault prediction is an important activity in the testing phase of the software development life cycle and involves various statistical and machine learning techniques. These techniques are useful for making accurate predictions to improve software quality. Researchers have used different techniques on different datasets to build fault prediction in software projects, but these techniques vary and are not generalised. As a result, it creates challenges that make it difficult to choose a suitable technique for software fault prediction in a particular context or project. This mapping study focuses on research published from 1997 to 2020 involving fault prediction techniques, intending to determine a classification of fault prediction techniques based on problem types that researchers need to solve. This study conducted a systematic mapping study to structure and categorise the research evidence that has been published in fault prediction. A total of 82 papers are mapped to a classification scheme. This study identified research gaps and specific issues for practitioners, including the need to classify fault prediction techniques according to problem types and to provide a systematic way to identify suitable techniques for fault prediction models.

Predictive Reliability and Fault Management in Exascale Systems

Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.

Best Suited Machine Learning Techniques for Software Fault Prediction

In this world of emerging applications of software, it is always important to provide a quality assured product to customers. Software Fault Prediction popularly abbreviated as SFP is a major field which helps to provide quality assured products to customers. It helps to recognize modules that are bug- free and bug-prone in a software module. Machine learning techniques for both classification and determination are used for the purpose of software fault prediction. Software Fault Prediction is carried out prior to testing process without executing the source code, instead vital characteristics of software is taken into consideration. This early identification of faults can help software engineers to reduce the risk of system failure. A company does not always prefer to invest more expense on testing and in those situations, software fault prediction can have an upper hand in testing. The software fault prediction model will first train the learning techniques to generate base learners and then apply these base learners to unseen projects. It is always preferred to determine the count of faults rather than classifying each software module as fault-free and fault-prone. All software fault prediction techniques depend on base learners used and also nature of fault dataset. In this paper, the major learning techniques to determine software fault, characteristics of software fault dataset, etc. are discussed.

Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

SummarySoftware fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.

Extracting Distribution Network Fault Semantic Labels From Free Text Incident Tickets

Increased monitoring of distribution networks and power system assets present utilities with new opportunities to predict and forestall system failures. Although automated pattern recognition methodologies have given other industries significant advantage, power system operators face additional challenges before these can be realized. The effort of apportioning ground truth to fault data creates a knowledge bottleneck that can make utilizing automatic classification techniques impossible. Surrogate approaches using operational process outputs such as maintenance tickets as labels can be challenging owing to the causal ambiguity of these written records. To approach a solution, this paper demonstrates utilizing natural language processing techniques to disambiguate the free text in maintenance tickets for onward use in supervised learning of fault prediction and classification techniques. A demonstration of this approach on an established power quality fault data set is provided for illustration.

Dynamic Fault Prediction of Power Transformers Based on Hidden Markov Model of Dissolved Gases Analysis

Dissolved gases analysis (DGA) provides widely recognized practice for oil-immersed power transformers, and it is mainly interpreted for fault diagnosis. In order to accurately estimate the health index state of power transformers and predict the incipient operation failure, a dynamic fault prediction technique based on hidden Markov model (HMM) of DGA is proposed in this paper. Gaussian mixture model, as a soft clustering method, is used to extract the static features of different health states from a DGA dataset of 65 in-service power transformers with 1600 days operation. Especially, a sub-health state is introduced to enrich the health index and aging stages of power transformers. The static features between health states and concentrations of dissolved gases are built, and the effectiveness of clustering is cross validated. Furthermore, taking time sequence into consideration, transition probability of power transformer between different health states based on the HMM model is calculated and analyzed. The effectiveness of dynamic early warning and incipient fault prediction in sub-health status of in-service power transformers has been proved. Moreover, the dynamic fault prediction is able to provide decision-making basis for practical condition-based operation and maintenances.

A study on software fault prediction techniques

Software fault prediction aims to identify fault-prone software modules by using some underlying properties of the software project before the actual testing process begins. It helps in obtaining desired software quality with optimized cost and effort. Initially, this paper provides an overview of the software fault prediction process. Next, different dimensions of software fault prediction process are explored and discussed. This review aims to help with the understanding of various elements associated with fault prediction process and to explore various issues involved in the software fault prediction. We search through various digital libraries and identify all the relevant papers published since 1993. The review of these papers are grouped into three classes: software metrics, fault prediction techniques, and data quality issues. For each of the class, taxonomical classification of different techniques and our observations have also been presented. The review and summarization in the tabular form are also given. At the end of the paper, the statistical analysis, observations, challenges, and future directions of software fault prediction have been discussed.

Towards an ensemble based system for predicting the number of software faults

Software fault prediction using different techniques has been done by various researchers previously. It is observed that the performance of these techniques varied from dataset to dataset, which make them inconsistent for fault prediction in the unknown software project. On the other hand, use of ensemble method for software fault prediction can be very effective, as it takes the advantage of different techniques for the given dataset to come up with better prediction results compared to individual technique. Many works are available on binary class software fault prediction (faulty or non-faulty prediction) using ensemble methods, but the use of ensemble methods for the prediction of number of faults has not been explored so far. The objective of this work is to present a system using the ensemble of various learning techniques for predicting the number of faults in given software modules. We present a heterogeneous ensemble method for the prediction of number of faults and use a linear combination rule and a non-linear combination rule based approaches for the ensemble. The study is designed and conducted for different software fault datasets accumulated from the publicly available data repositories. The results indicate that the presented system predicted number of faults with higher accuracy. The results are consistent across all the datasets. We also use prediction at level l (Pred(l)), and measure of completeness to evaluate the results. Pred(l) shows the number of modules in a dataset for which average relative error value is less than or equal to a threshold value l. The results of prediction at level l analysis and measure of completeness analysis have also confirmed the effectiveness of the presented system for the prediction of number of faults. Compared to the single fault prediction technique, ensemble methods produced improved performance for the prediction of number of software faults. Main impact of this work is to allow better utilization of testing resources helping in early and quick identification of most of the faults in the software system.

A decision tree logic based recommendation system to select software fault prediction techniques

Identifying a reliable fault prediction technique is the key requirement for building effective fault prediction model. It has been found that the performance of fault prediction techniques is high...

Software Module Fault Prediction using Convolutional Neural Network with Feature Selection

Software plays a significant role in technological and economic development due to its utmost importance in day to day activities. A sequence of rigorous activities under certain constraints is followed to come up with reliable software. Various measures are taken during the process of software development to ensure high quality software. One such method is software module fault prediction for quality assurance to discover defects in the software prior to testing. It aids in predicting the software module faults earlier in the development of the software which predicts fault prone modules so that these can be given special attention to avoid any future risk which eventually curbs the testing along with maintenance cost and effort. The literature survey uncovers many findings that had never been focused like dimensionality reduction and feature selection based on individual feature importance which leads to increase in time complexity and chances of false information. This paper addresses these issues and proposes a supervised machine learning based software module fault prediction technique by implementing Convolutional Neural Network (CNN) as classifier model. Feature selection methods used are InfoGain and Correlation. The results obtained are compared with the existing method HySOM (SOM Clustering with Artificial Neural Network Classification) by considering three different feature sets (Fifteen features, Eighteen features and Twenty one features) of PC1 dataset from NASA. The comparative analysis is performed on the basis of accuracy, precision, recall and F1-measure. The results clearly show better performance of the proposed CNN based technique than HySOM. This paper will contribute towards improvement of quality assurance models utilized for software fault prediction by automating this process using machine learning which enhances True Positive Rate and reduces the detection error. This in turn will help project managers, testers and developers to locate and keep track of fault prone modules so that final software is more accurate, consistent and reliable without consuming much of the testing and maintenance resources.

Empirical validation for effectiveness of fault prediction technique based on cost analysis framework

Prediction of the faulty or non-faulty modules in a software is an important task in software development life cycle and often carried out by validating them through certain empirical techniques. A good number of techniques have been proposed by different authors to develop fault prediction models and determine best suitable techniques for fault prediction based on certain performance criteria. However it appears that there is a scope to extend the research by giving emphasis on feasibility analysis of fault prediction techniques. In this paper, a cost evaluation framework has been proposed to evaluate the effectiveness of developed fault prediction models. Statistical methods such as linear regression, logistic regression, polynomial regression, Naive Bayes and support vector machine have been applied to develop a classifier in order to predict as to whether any module is a faulty or non-faulty one for an embedded software system. The proposed approach has been applied on five different public domain software system chosen from NASA database consisting of different number of faulty models. The performance of the predicted models are evaluated using different performance evaluation parameters. From cost-based analysis, experimental results reveal that our fault prediction model is best suitable for projects with faulty modules less than a certain threshold value depending on cost and fault identification efficiency of different testing phases. The results also show that the model developed using logistic regression technique obtained promising results in terms of performance and cost when compared with other applied techniques.

Machine Learning Based Approach for Predicting Fault in Software Engineering by GMFPA: A Survey

Fault propensity of software is the prospect that the component contains faults. To forecast fault proneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, etc. Machine learning techniques can be used to analyze data from different perspectives and enable developers to retrieve useful information. Machine learning techniques are proven to be useful in terms of software bug prediction. This is leading to increase in development of machine learning methods for exploring data sets, which can be used in constructing models for predicting quality attributes such as fault proneness. This research work analyzed various fault prediction techniques and proposed a new algorithm GMFPA (Genetic Metric Fault Prediction Algorithm) to explore the fault prone modules by using metrics. The GMFPA is used to identify fault with highest gain value by forming the hypothesis set.

Linear and non-linear heterogeneous ensemble methods to predict the number of faults in software systems

Several classification techniques have been investigated and evaluated earlier for the software fault prediction. These techniques have produced different prediction accuracy for the different software systems and none of the technique has always performed consistently better across different domains. On the other hand, software fault prediction using ensemble methods can be very effective, as they take the advantage of each participating technique for the given dataset and try to come up with better prediction results compared to the individual techniques. Many works are available for classifying software modules being faulty or non-faulty using the ensemble methods. These works are only specifying that whether a given software module is faulty or not, but number of faults in that module are not predicted by them. The use of ensemble methods for the prediction of number of faults has not been explored so far. To fulfill this gap, this paper presents ensemble methods for the prediction of number of faults in the given software modules. The experimental study is designed and conducted for five open-source software projects with their fifteen releases, collected from the PROMISE data repository. The results are evaluated under two different scenarios, intra-release prediction and inter-releases prediction. The prediction accuracy of ensemble methods is evaluated using absolute error, relative error, prediction at level l, and measure of completeness performance measures. Results show that the presented ensemble methods yield improved prediction accuracy over the individual fault prediction techniques under consideration. Further, the results are consistent for all the used datasets. The evidences obtained from the prediction at level l and measure of completeness analysis have also confirmed the effectiveness of the proposed ensemble methods for predicting the number of faults.

An empirical study of some software fault prediction techniques for the number of faults prediction

During the software development process, prediction of the number of faults in software modules can be more helpful instead of predicting the modules being faulty or non-faulty. Such an approach may help in more focused software testing process and may enhance the reliability of the software system. Most of the earlier works on software fault prediction have used classification techniques for classifying software modules into faulty or non-faulty categories. The techniques such as Poisson regression, negative binomial regression, genetic programming, decision tree regression, and multilayer perceptron can be used for the prediction of the number of faults. In this paper, we present an experimental study to evaluate and compare the capability of six fault prediction techniques such as genetic programming, multilayer perceptron, linear regression, decision tree regression, zero-inflated Poisson regression, and negative binomial regression for the prediction of number of faults. The experimental investigation is carried out for eighteen software project datasets collected from the PROMISE data repository. The results of the investigation are evaluated using average absolute error, average relative error, measure of completeness, and prediction at level l measures. We also perform Kruskal–Wallis test and Dunn’s multiple comparison test to compare the relative performance of the considered fault prediction techniques.

Software Fault Prediction Using Two -Stage Data Preprocessing Method

Software testing plays important role in software development life cycle (SDLC) while dealing with critical business applications. From SDLC 70% of time and cost is used for testing only, it is time consuming and costly phase. For this one of the cost effective technique can be used which predicts a module as fault-prone and non-fault prone prior to testing. Predicting a safe module during testing and after development increases the cost of projects. As need to allocate better-test resources for module. This process may take most of the time in preparation and may lose some modules untested which cause accidental failure. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. In this research, we present a novel fault prediction technique that reduces the probability of false (pf) and increases the precision for detection of faulty modules. Software Reliability is becoming an essential attribute of any software system. To improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of fault-prone modules.Software defect prediction model helps in early detection of faults and contribute to their efficient removal and producing a reliable software system.