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When it comes to software development, precise planning, proper documentation and proper process control, some errors are inevitable in the software environment. These software flaws can lead to quality deterioration, which can be the main reason behind system failure. As the whole world especially developing countries is dependent upon software systems, it is very important to focus on its reliability aspect. Nowadays sophisticated systems require concerted efforts for managing and reducing the shortcomings in software engineering. But, these efforts require more cost, more money and more time. Software error prediction is the most helpful step in the testing stage of the software development life cycle. It identifies components or parts of the code where an error may occur and requires broad testing, so the test resources can be efficiently used. Software error assessment reduces efforts of testing the software by helping the software testers locate the actual problem and classify different classes of errors in the system. Error estimators are majorly used in various organizations to evaluate the software to save time, improve the quality of software and testing and optimize resources to meet timelines. Machine learning provides support in fault projection by collecting the training data from various edge devices and thus helps in escalating the reliability of the software available on Kaggle. The multilayer perceptron shows better results in precision, recall, F1 score and accuracy as compared to decision tree and Gaussian Naive Bayes as it achieves an accuracy of 96.8%. [ABSTRACT FROM AUTHOR]

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. [ABSTRACT FROM AUTHOR]

Emerging technologies in artificial intelligence (AI) and advanced optimization methodologies have opened up a new frontier in the field of software engineering. Among these methodologies, optimization algorithms such as the multi-verse optimizer (MVO) provide a compelling and structured technique for identifying software vulnerabilities, thereby enhancing software robustness and reliability. This research investigates the feasibility and efficacy of applying an augmented version of this technique, known as the test path distance multiverse optimization (TPDMVO) algorithm, for comprehensive path coverage testing, which is an indispensable aspect of software validation. The algorithm's versatility and robustness are examined through its application to a wide range of case studies with varying degrees of complexity. These case studies include rudimentary functions like maximum and middle value extraction, as well as more sophisticated data structures such as binary search trees and AVL trees. A notable innovation in this research is the introduction of a customized fitness function, carefully designed to guide TPDMVO towards traversing all possible execution paths in a program, thereby ensuring comprehensive coverage. To further enhance the comprehensiveness of the test, a metric called 'test path distance' (TPD) is utilized. This metric is designed to guide TPDMVO towards paths that have not been explored before. The findings confirm the superior performance of the TPDMVO algorithm, which achieves complete path coverage in all test scenarios. This demonstrates its robustness and adaptability in handling different program complexities. [ABSTRACT FROM AUTHOR]

An optimal software release strategy is a well-investigated issue in software reliability literature. Comprehensive testing is expected before releasing the software into the market to enhance the reliability and security of the software device. In recent years, few analysts have recommended the scheme for software projects that support releasing the software early in the market and continue the testing process for an added period in the field environment even after the software is distributed. These studies are based on one common assumption that the efficiency of the software engineers in detecting the faults occurs at a consistent rate throughout the testing phase. However, bug-identification rate may experience discontinuity at the software release time. In software engineering, the time-point at which fault detection rate changes is termed as change-point. Consequently, an alternative software release policy is proposed in the present paper, which offers a generalized framework for fault detection phenomenon using the unified approach. An extensive analysis of software time-to-market and testing duration based on cost-efficiency and reliability measures is discussed by considering the change in tester's fault detection rate. A multi-criteria decision making technique known as multi-attribute utility theory is applied to optimize the software release policy under field-testing (FT) and no field-testing (NFT) frameworks. The relevance of the optimization problem is illustrated using a numerical example, comprising both the exponential and S-shaped bug-detection process. [ABSTRACT FROM AUTHOR]

Effort‐Aware Defect Prediction (EADP) methods sort software modules based on the defect density and guide the testing team to inspect the modules with high defect density first. Previous studies indicated that some feature selection methods could improve the performance of Classification‐Based Defect Prediction (CBDP) models, and the Correlation‐based feature subset selection method with the Best First strategy (CorBF) performed the best. However, the practical benefits of feature selection methods on EADP performance are still unknown, and blindly employing the best‐performing CorBF method in CBDP to pre‐process the defect datasets may not improve the performance of EADP models but possibly result in performance degradation. To assess the impact of the feature selection techniques on EADP, a total of 24 feature selection methods with 10 classifiers embedded in a state‐of‐the‐art EADP model (CBS+) on the 41 PROMISE defect datasets were examined. We employ six evaluation metrics to assess the performance of EADP models comprehensively. The results show that (1) The impact of the feature selection methods varies in classifiers and datasets. (2) The four wrapper‐based feature subset selection methods with forwards search, that is, AdaBoost with Forwards Search, Deep Forest with Forwards Search, Random Forest with Forwards Search, and XGBoost with Forwards Search (XGBF) are better than other methods across the studied classifiers and the used datasets. And XGBF with XGBoost as the embedded classifier in CBS+ performs the best on the datasets. (3) The best‐performing CorBF method in CBDP does not perform well on the EADP task. (4) The selected features vary with different feature selection methods and different datasets, and the features noc (number of children), ic (inheritance coupling), cbo (coupling between object classes), and cbm (coupling between methods) are frequently selected by the four wrapper‐based feature subset selection methods with forwards search. (5) Using AdaBoost, deep forest, random forest, and XGBoost as the base classifiers embedded in CBS+ can achieve the best performance. In summary, we recommend the software testing team should employ XGBF with XGBoost as the embedded classifier in CBS+ to enhance the EADP performance. [ABSTRACT FROM AUTHOR]

Software Industry is evolving at a very fast pace since last two decades. Many software developments, testing and test case generation approaches have evolved in last two decades to deliver quality products and services. Testing plays a vital role to ensure the quality and reliability of software products. In this paper authors attempted to conduct a systematic study of testing tools and techniques. Six most popular e-resources called IEEE, Springer, Association for Computing Machinery (ACM), Elsevier, Wiley and Google Scholar to download 738 manuscripts out of which 125 were selected to conduct the study. Out of 125 manuscripts selected, a good number approx. 79% are from reputed journals and around 21% are from good conference of repute. Testing tools discussed in this paper have broadly been divided into five different categories: open source, academic and research, commercial, academic and open source, and commercial & open source. The paper also discusses several benchmarked datasets viz. Evosuite 10, SF100 Corpus, Defects4J repository, Neo4j, JSON, Mocha JS, and Node JS to name a few. Aim of this paper is to make the researchers aware of the various test case generation tools and techniques introduced in the last 11 years with their salient features. [ABSTRACT FROM AUTHOR]

Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, software effort prediction (SEP) and software test prediction (STP) (i.e., software reliability problems) are tackled by integrating the salp swarm algorithm (SSA) with a backpropagation neural network (BPNN). Software effort and test prediction problems are common in software engineering and arise when seeking to determine the actual software resources needed to develop a project. BPNN is the most popular prediction algorithm used in the literature. The performance of BPNN depends totally on the initial parameter values such as weight and biases. The main objective of this paper is to integrate SSA with the BPNN to find the optimal weight for every training cycle and thereby improve prediction accuracy. The proposed method, abbreviated as SSA-BPNN, is tested on twelve SEP datasets and two STP datasets. All datasets vary in terms of complexity and size. The results obtained by SSA-BPNN are evaluated according to twelve performance measures: MSE, RMSE, RAE, RRSE, MAE, MRE, MMRE, MdMRE, VAF(%), R2(%), ED, and MD. First, the results obtained by BPNN with SSA (i.e., SSA-BPNN) and without SSA are compared. The evaluation of the results indicates that SSA-BPNN performs better than BPNN for all datasets. In the comparative evaluation, the results of SSA-BPNN are compared against thirteen state-of-the-art methods using the same SEP and STP problem datasets. The evaluation of the results reveals that the proposed method outperforms the comparative methods for almost all datasets, both SEP and STP, in the case of most performance measures. In conclusion, integrating SSA with BPNN is a very powerful approach for solving software reliability problems that can be used widely to yield accurate prediction results. [ABSTRACT FROM AUTHOR]

Standard software testing might not catch important defects, and formal methods can be difficult to use. But, there's a middle ground between the two. This middle ground involves adding five steps to standard testing and employing test randomization, model-based testing, and a more aggressive use of assertions (also called self-tests). [ABSTRACT FROM PUBLISHER]

The Software Operational Profile (SOP) is a software specification based on how users use the software. This specification corresponds to a quantitative representation of the software that identifies its most used parts. As software reliability depends on the context in which users operate the software, the SOP is used in software reliability engineering. However, there is evidence of a misalignment between the software tested parts and the SOP. Therefore, this paper investigates a potential misalignment between SOP and the tested software parts to obtain more evidence of this misalignment based on experimental data. We performed a set of Experimental Studies - EXS to verify: a) whether there are significant variations in how users operate the software; b) whether there is a misalignment between the SOP and the tested software parts; c) whether failures occur in untested SOP parts in case of misalignment; d) whether a test strategy based on the amplification of the existent test set with additional test data generated automatically can contribute to reduce the misalignment between SOP and untested software parts. We collected data from four software while users were operating them. We analyzed this data to reach the goals of this work. The results show that there is significant variation in how users operate software and that there is a misalignment between SOP and the tested software parts after evaluating the four software studied. There is also indication of failures in the untested SOP parts. Although the aforementioned test strategy has reduced the potential misalignment, the test strategy is not enough to avoid it, thus indicating a need for specific test strategies using SOP as a test criterion. These results indicate that SOP is relevant not only to software reliability engineering but also to testing activities, regardless of the adopted testing strategy. [ABSTRACT FROM AUTHOR]

Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based Multi-Objective Evolutionary Algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs. [ABSTRACT FROM AUTHOR]

In present situation, computer and internet are plays a significant role among people. Generally, enormous amount of software's are developed by industries for satisfying the needs of customer and user which also helps to enhance their facilities. In the development of network filed, errors, faults, failures, system complexity, compromising software reliability and quality are considered as major issues. So, the software testing is necessary to maintain customer's reliability and to ensure the quality of the product. Software testing helps to improve and find defects that are made during the development phases. The Software Fault Prediction (SFP) is the one of the essential and main research topic in the area of software engineering. According to software modules, software fault prediction handles the limited test resources in an efficient manner by effective prediction of fault proneness. Some resource and research methods are implemented with classification methods for separating the software modules into faulty and non-faulty. Based on features or metrics collected from each software modules, the results are provided by mining of software development repositories. Our research work is carried out to provide better software reliability, quality and fault prediction through software testing. [ABSTRACT FROM AUTHOR]

The theory of test data selection proposed by Goodenough and Gerhart is examined. In order to extend and refine this theory, the concepts of a revealing test criterion and a revealing subdomain are proposed. These notions are then used to provide a basis for constructing program tests. A subset of a program's input domain is revealing if the existence of one incorrectly processed input implies that all of the subset's elements axe processed incorrectly. The intent of this notion is to partition the program's domain in such a way that all elements of an equivalence class are either processed correctly or incorrectly. A test set is then formed by choosing one element from each class. This process represents perfect program testing. For a practical testing strategy, the domain is partitioned into subdomains which are revealing for errors considered likely to occur. Three programs which have previously appeared in the literature are discussed and tested using the notions developed in the paper. [ABSTRACT FROM AUTHOR]

This paper provides a state-of-the-art review of cloud testing. Cloud computing, a new paradigm for developing and delivering computing applications and services, has gained considerable attention in recent years. Cloud computing can impact all software life cycle stages, including the area of software testing. TaaS (Testing as a Service) or cloud testing, which includes testing the cloud and testing using the cloud, is a fast developing area of research in software engineering. The paper addresses the following three areas: (1) general research in cloud testing, (2) specific cloud testing research, i.e., tools, methods, and systems under test, and (3) commercial TaaS tools and solutions. [ABSTRACT FROM AUTHOR]

In this paper, we propose two general frameworks for deriving several software reliability growth models based on a non-homogeneous Poisson process (NHPP) in the presence of imperfect debugging and error generation. The proposed models are initially formulated for the case when there is no differentiation between failure observation and fault removal testing processes, and then extended for the case when there is a clear differentiation between failure observation and fault removal testing processes. During the last three decades, many software reliability growth models (SRGM) have been developed to describe software failures as a random process, and can be used to evaluate development status during testing. With SRGM, software engineers can easily measure (or forecast) the software reliability (or quality), and plot software reliability growth charts. It is not easy to select the best model from a plethora of models available. There are few SRGM in the literature of software engineering that differentiates between failure observation and fault removal processes. In real software development environments, the number of failures observed need not be the same as the number of faults removed. Due to the complexity of software systems, and an incomplete understanding of software, the testing team may not be able to remove the fault perfectly on observation of a failure, and the original fault may remain, resulting in a phenomenon known as imperfect debugging, or get replaced by another fault causing error generation. In the case of imperfect debugging, the fault content of the software remains the same; while in the case of error generation, the fault content increases as the testing progresses. Removal of observed faults may result in the introduction of new faults. [ABSTRACT FROM AUTHOR]

Nonhomogeneous poisson process based software reliability growth models are generally classified into two groups. The first group contains models, which use the machine execution time or calendar time as a unit of fault detection/removal period. Such models are called continuous time models. The second group contains models, which use the number of test occasions/cases as a unit of fault detection period. Such models are called discrete time models, since the unit of software fault detection period is countable. A large number of models have been developed in the first group while there are fewer in the second group. Discrete time models in software reliability are important and a little effort has been made in this direction. In this paper, we develop two discrete time SRGMs using probability generating junction for the software failure occurrence/fault detection phenomenon based on a NHPP namely, basic and extended models. The basic model exploits the fault detection/removal rate during the initial and final test cases. Whereas, the extended model incorporates fault generation and imperfect debugging with learning. Actual software reliability data have been used to demonstrate the proposed models. The results are fairly encouraging in terms of goodness-of-fit and predictive validity criteria due to applicability and flexibility of the proposed models as they can capture a wide class of reliability growth curves ranging from purely exponential to highly S-shaped. [ABSTRACT FROM AUTHOR]

Software testing can be viewed as an inductive inference process during which the tester attempts to deduce software properties from its behaviour on a finite number of test cases. This paper investigates the foundation of software testing by interpreting the axioms of test adequacy criteria as properties of inductive inference. The interpretation manifests the conservative and simplest hypothesis nature of the induction underlying software testing. It also yields results relating adequate testing to software correctness and reliability. The convergence of the inductive inference process is proved to he a condition of the correctness of tested software. By measuring the convergence according to the probability of an inference result being correct up to a given error rate, a new approach to software reliability estimation is proposed, which differs from existing ones in taking software complexity into account. [ABSTRACT FROM AUTHOR]