Software Fault Research Articles

Software Bug Prediction Using Machine Learning

Software bug prediction is a crucial aspect of software development and maintenance as it directly impacts the overall success of the software. By identifying potential bugs early on, software quality, reliability, and efficiency can be improved while also helping to reduce costs. Creating a reliable bug prediction model is a complex task, with various techniques proposed in the literature. In this paper, a bug prediction model utilizing machine learning algorithms is introduced. Three supervised machine learning algorithms have been incorporated to forecast future software faults using historical data. The evaluation process revealed that Machine Learning algorithms, specifically Naïve Bayes (NB), Decision Tree (DT), and Artificial Neural Networks (ANNs), can be effectively utilized with a high level of accuracy. Additionally, a comparison measure was implemented to assess the proposed prediction model against other methods. The findings indicated that the Machine Learning approach outperformed other techniques in terms of performance. Key Words: Bug Prediction, Reliability, Efficiency, Quality, Machine Learning, Performance Metrics

ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)

Software stands out as one of the most rapidly evolving technologies in the present era, characterized by its swift expansion in both scale and complexity, which leads to challenges in quality assurance. Software defect prediction (SDP) has emerged as a methodology crafted to anticipate undiscovered defects, leveraging known defect data from existing codes. This methodology serves to facilitate software quality management, thereby ensuring overall product quality. The methodologies of machine learning (ML) and one of its branches, deep learning (DL), exhibit superior accuracy and adaptability compared to traditional statistical approaches, catalyzing active research in this domain. However, it makes it hard to generalize, not only because of the disparity between open-source projects and commercial projects but also due to the differences in each industrial sector. Consequently, further research utilizing datasets sourced from diverse real-world sectors has become imperative to bolster the applicability of these findings. For this study, we utilized embedded software for use with the telecommunication systems of Samsung Electronics, supplemented by the introduction of nine novel features to train the model, and a subsequent analysis of the results ensued. The experimental outcomes revealed that the F-measurement metric has been enhanced from 0.58 to 0.63 upon integration of the new features, thereby signifying a performance augmentation of 8.62%. This case study is anticipated to contribute to bolstering the application of SDP methodologies within analogous industrial sectors.

Cross-project concurrency bug prediction using domain-adversarial neural network

In recent years, software bug prediction has shown to be effective in narrowing down the potential bug modules and boosting the efficiency and precision of existing testing and analysis tools. However, due to its non-deterministic nature and low presence, concurrency bug labeling is a challenging task, which limits the implementation of within-project concurrency bug prediction. This paper proposes DACon, a Domain-Adversarial neural network-based cross-project Concurrency bug prediction approach to tackle this problem by leveraging information from another related project. By combining a set of designed concurrency code metrics with widely used sequential code metrics, DACon uses SMOTE (Synthetic Minority Over-sampling TEchnique) and domain-adversarial neural network to mitigate two challenges including the severe class imbalance between concurrency bug-prone samples and concurrency bug-free samples, and shift between source and target distribution during bug prediction implementation. Our evaluation on 20 pair-wise groups of experiments constructed from 5 real-world projects indicates that cross-project concurrency bug prediction is feasible, and DACon can effectively predict concurrency bugs across different projects.

Delineating patient errors in an intracavernosal injection program.

Intracavernosal injection therapy (ICI) is a well-established therapeutic strategy for men with erectile dysfunction. Complications are often related to patient error when performong ICI. The objective of this study was to examine patient errors in an established patient training program for performing ICI and identify factors that could predict major errors. Patients enrolled in our ICI program are trained on technical aspects, and dose titration is begun. Patients are given explicit instructions during training, both verbally and in written form. Records were reviewed for men using ICI for ≥6months. Multivariable analysis was used to define predictors of major errors. Errors were listed as minor (zero-response injection, penile bruising, expired medication) and major (errors potentially leading to priapism: dose self-titration, double injecting). Overall, 1368 patients met the inclusion criteria and were included in the analysis. The mean patient age was 66 ± 22 (range 29-91) years. Regarding education, 41% of patients had graduate-level education, 48% had college education, and 11% high school education. Mean follow-up was 3.2 ± 7.6 (range 0.5-12) years. The agents used were trimix (62%), bimix (35%), papaverine (2%), and prostaglandin E1 monotherapy (1%). At least 1 error occurred during self-administration in 42% of patients during their time in the program. Errors included zero response to medication due to technical error (8% of patients), penile bruising (34%), use of an expired bottle (18%), self-titration (5%), and double injecting (4% of patients); 12% of men committed ≥1 error during their time in the program. On multivariable analysis, independent predictors of the occurrence of a major error included: young age, graduate-level education, and <12months of injection use. To the best of our knowledge, this is the first reported study to investigate ICI errors and risk factors. The identification of factors predictive of major errors allows for more tailored and intensive training in this subset of patients. Strengths of this study include a large patient population (1386 men) with a considerable follow-up time. Additionally, the rigorous training, education, and monitoring of the participants, as well as the use of formal definitions, enhances the accuracy and reliability of the results. Despite the strengths of the study, recall bias may be a limitation concern. The majority of patients were error free, and the majority of the errors were minor in nature. Major errors occurred in <10% of patients. Younger age, graduate-level education, and less experience with ICI were independent predictors of major errors.

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1-measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.

Investigating levels of remote operation in high-level on-road autonomous vehicles using operator sequence diagrams

The continuing development of autonomous vehicle technology is making the presence of fully autonomous vehicles (SAE Level 5 of Driving Automation) on the road an ever more likely possibility. Similarly, regulation changes show countries are preparing for autonomous vehicles to increase their presence on public roads for both testing and use after sale. With this in mind, solutions to the problem of disengagement from the autonomous driving system by Level 5 vehicles, due to damage, operation outside of expected parameters or software failure among other reasons are being investigated including remote operation. This research aims to give evidence for the inclusion of remote operation into the autonomous driving and define the types of remote operation that may occur from existing literature. The four types of remote operation are Remote Monitoring, Remote Assistance, Remote Management and Remote Driving. Operator sequence diagrams are used to evaluate these types of remote operation in likely scenarios they may occur and draw conclusions about the role and the tasks the operator will be required to complete.

Improving with Hybrid Feature Selection in Software Defect Prediction

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.

Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model

Testing and debugging have been the most significant steps of software development since it is tricky for engineers to create error-free software. Software testing takes place after coding with the goal of finding flaws. If errors are found, debugging would be done to identify the source of the errors so that they may be fixed. Detecting as well as locating defects are thus two essential stages in the creation of software. We have created a unique approach with the following two working phases to generate a minimized test suite that is capable of both detecting and localizing faults. In the initial test suite minimization process, the cases were generated and minimized based on the objectives such as D-score and coverage by the utilization of the proposed Blue Monkey Customized Black Widow (BMCBW) algorithm. After this test suite minimization, the fault validation is done which includes the process of fault detection and localization. For this fault validation, we have utilized an improved Long Short-Term Memory (LSTM). At 90% of the learning rate the accuracy of the presented work is 0.97%, 2.20%, 2.52%, 0.97% and 2.81% is better than the other extant models like AOA, COOT, BES, BMO and BWO methods. The results obtained proved that our Blue Monkey Customized Black Widow Optimization-based fault detection and localization approach can provide superior outcomes.

Optimized Ensemble Techniques For Precise Software Error Detection

Optimized Ensemble Techniques For Precise Software Error Detection

Deep learning based continuous integration and continuous delivery software defect prediction with effective optimization strategy

Software defect prediction is one of the most difficult tasks in the IT sector. Continuous Integration and Continuous Delivery (CI/CD) software defect prediction is used in earlier stage, which consumes less amount of time. Various learning algorithms, such as convolutional neural networks and other machine learning algorithms (ML), are employed to forecast the flaws in the software model. In these existing algorithms, some issues are noticed, such as high computational complexity, excessive time consumption, the need for more energy to predict the model and a high-loss function. To address these issues, a novel deep learning (DL)-based CI/CD software defect prediction technique applying an effective optimization strategy is provided to improve the model's efficiency. The numerical data are collected from open source, and the obtained data is initially labeled based on time domain limitations. Initially, the Modified Synthetic Minority Over-Sampling Technique (M-SMOTE) mechanism is applied to balance the data to avoid overfitting problems, and data normalization is performed to rescale and normalize the data properly. After moralization, the optimal set of features is extracted from the data using Focal Bidirectional Encoder Representations from Transformers (F-BERT) to enhance the efficiency of the model. Finally, the software defects are predicted using Bidirectional Long Short-Term Memory (Bi-LSTM) integrated into the convolutional Gated recurrent units (GRU) model (Bi-CGRU) based on collected features. Hybrid Levy Rao (HLR) optimization is used to tune the hyperparameters properly in the classifier model. The proposed model's performance indicators are examined. The proposed model has a 95.32 % accuracy, a 93.3 % recall, a 94.98 % Matthews correlation coefficient, and an F1-score of 91.355 %. The proposed model generates less labeling noise and wait time than existing methods.

Method-level Bug Prediction: Problems and Promises

Fixing software bugs can be colossally expensive, especially if they are discovered in the later phases of the software development life cycle. As such, bug prediction has been a classic problem for the research community. As of now, the Google Scholar site generates ∼113,000 hits if searched with the “bug prediction” phrase. Despite this staggering effort by the research community, bug prediction research is criticized for not being decisively adopted in practice. A significant problem of the existing research is the granularity level (i.e., class/file level) at which bug prediction is historically studied. Practitioners find it difficult and time-consuming to locate bugs at the class/file level granularity. Consequently, method-level bug prediction has become popular in the past decade. We ask, are these method-level bug prediction models ready for industry use? Unfortunately, the answer is no . The reported high accuracies of these models dwindle significantly if we evaluate them in different realistic time-sensitive contexts. It may seem hopeless at first, but, encouragingly, we show that future method-level bug prediction can be improved significantly. In general, we show how to reliably evaluate future method-level bug prediction models and how to improve them by focusing on four different improvement avenues: building noise-free bug data, addressing concept drift, selecting similar training projects, and developing a mixture of models. Our findings are based on three publicly available method-level bug datasets and a newly built bug dataset of 774,051 Java methods originating from 49 open-source software projects.

PCG: A joint framework of graph collaborative filtering for bug triaging

AbstractBug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering‐based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios.

Decision Making on a Software Upgrade or Decommission with Data Mining and Machine Learning Techniques in Information Technology Industry

The Organizations have been investing more in Technology and Infrastructure spends like software upgrades, software renewals, software replacements, platform migrations etc., apart from investment in Business, People, and Processes. In this context, it is not an easy task for stakeholders to decide whether to go for a software upgrade or to replace it with another software. There is no unified approach or solution to consolidate data and relationships of Information Technology Assets, Software Upgrades, Software costs, Software defects, Software Performance Metrics, Security issues, IT system versions, service level objectives etc. Due to this, the decision making of software upgrades and software decommissioning is a tedious process and takes more time and effort. There is a need to build a solution that can integrate and validate the information like software assets, software upgrade success and failure likelihoods, cost benefit analysis of Cloud Computing, software metrics for fault prediction, software maintainability prediction results, Digital Transformation readiness and other related factors. There is an opportunity to apply Machine Learning techniques in defining and deriving the success likelihoods on the following data: Systems and data integration, software assets compatibility, operational service level agreement breaches, quality assurance metrics, security issues, number of open defects, number of defect fixes, number of priority incidents, mean time to resolve critical incidents, expected cost increase in software maintenance, potential cost reduction with the software or hardware replacement etc. This Research Proposal outlines the above mentioned to build a recommendation system aka decision tree namely Software Upgrades or Decommissions Life Cycle.

Advancing modern code review effectiveness through human error mechanisms

Modern code reviews tend to take a lightweight process, in which the accuracy and efficiency of identifying defects rely heavily on code reviewers’ experience. The human errors of developers, as a significant cause of software defects, is a key to identifying defects. However, there is a lack of understanding of the human error mechanisms underlying defects in code. This paper proposes an innovative code review method for identifying defects by pinpointing the scenarios that developers tend to commit errors. The method was validated by two experimental studies that involved 40 participants of about 5 years’ programming experience and modest code review experience. The experiment shows that the proposed method has significantly improved True Positives and Sensitivity by about 400 %, improved Precision by approximately 200 %, and reduced around one-third of False Positives. The effects were consistent across different tasks and different code reviewers.

KCO: Balancing class distribution in just-in-time software defect prediction using kernel crossover oversampling.

The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by interpolating different data templates within the selected data clusters. According to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline techniques. The experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results.

Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately

Hybridization of fuzzy rough feature selection with ANFIS and turbulent flow of water optimization for managing software defect prediction uncertainty

By anticipating system defect-prone units, software-developing businesses aim to increase the quality of software. Despite the development of numerous Data Mining (DM) and Artificial Intelligence (AI) techniques in the Software Defect Prediction (SDP) field, dealing with the uncertainty of datasets persists due to noise, data distribution, class overlapping, proposed model parameters, and old data. This uncertainty issue has a negative impact on the accuracy of software defect prediction. To overcome this limitation, a model-based hybridization of Ant Colony Optimization-inspired Fuzzy Rough Feature Selection (FRAC) followed by adapting the parameters of Adaptive Neuro-Fuzzy Inference System (ANFIS) with a novel algorithm called Turbulent Flow of Water Optimization (TFWO) is recommended. The proposed model (FRAC+TFWANFIS) performed better than contemporary literature and other optimization algorithms in SDP, such as Ant Colony Optimization (ACO), Differential Evolution (DE), ANFIS, Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Also, the performance of the proposed model is superior to that of other conventional classification techniques such as Naïve Bayes (NB), Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy Rough Nearest Neighbor (FRNN), Fuzzy Nearest Neighbor (FNN), Bagging, C4.5, Random Forest (RF), and K-Nearest Neighbor (K-NN). Two datasets, PC3 and PC4, with large dimensions from the OPENML platform are used. The experiments are applied with regard to accuracy, Standard Deviation (SD), Root Mean Square Error (RMSE), Mean Square Error (MSE), and other measurement metrics. The uncertainty issue is addressed by the (FRAC+TFWANFIS) model with accuracy 90.8% and 91.1% for PC3 and PC4, respectively.

Predicting the Number of Software Faults using Deep Learning

The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

AbstractOf late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real‐time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT‐enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault‐detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy‐Aware Hierarchical Rule‐based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state‐of‐the‐art models.

Proactive Fault Tolerance in Distributed Cloud Systems: A Review of Predictive and Preventive Techniques

In a cloud computing environment, various hardware and software services are provided to the users across multiple servers and data centers. These servers are communicated to each other to allow greater scalability, flexibility, and reliability. Reliability is a vital factor in cloud computing that ensures that the requested services will be delivered to the users whenever they request them. However, different hardware or software faults may occur in cloud servers or data centers that prevent the users from receiving the service. Fault tolerance is defined as the ability of the system to provide services to the users even with the presence of faults or failures. In this review, we focused on some of the emerging fault tolerance techniques researchers have proposed to tackle the fault issues in cloud computing. We divided these techniques into three main categories: proactive and reactive techniques. Proactive techniques involve protecting the system defects by proposing certain procedures to prevent reaching the defective condition. Reactive techniques refer to the ability of the cloud system to recover the defective server or framework to continue working and providing the service.