Software Fault Research Articles

Various Software Testing Techniques, their Implementation and Significance on Banking Applications: In Context of Bangladesh

Abstract: Software testing is the process of identifying bugs or errors in developed software. Consequently, this process offers interested individuals with precise details on the product's quality. Software testing is done not only to ensure that the program behaves correctly or not, but to check whether it satisfies the user’s requirements and expectations as well. The testing of banking software is very important since they handle millions of transactions and are related to customers directly. Proper testing of the banking applications ensures that all operations are carried out accurately and remain safe and secure. The main objective of this paper is to provide an overview of various software testing techniques, their implementation and significance on the banking applications of Bangladesh.

Testing coverage-based software modeling incorporating random effect with change point and its release

The use of computers has been increasingly prevalent in our social lives in recent years. As a result, software engineers must create trustworthy software systems. Companies often release improved versions of the core program due to the constant demand and growing competition in the software field. A variety of growth models have been created to track and measure reliability by software managers and engineers. In the testing phase, the fault content and size of the software system increase, and consequently, the fault content found and eliminated during each debugging process decreases in comparison to the fault content present at the initial stage. In this scenario, we can consider the software fault detection process to be stochastic. The fault detection process is expressed in terms of testing coverage with random effects. In this study, we construct a testing coverage-based software model incorporating random effect with change point. We have used different testing coverage functions such as Exponential and Delayed S-shaped to study the effect of randomness. Further, multi-release planning for the proposed model has been studied and validated on the real-time failure dataset from Tandem Computers with four releases. Different performance measures and goodness-of-fit have been presented using a graphical representation.

A Study of The Effectiveness of Code Review in Detecting Security Vulnerabilities

Software flaws pose a severe danger to the security and privacy of computer systems and the people who use them [1]. For software systems to be reliable and available, vulnerabilities must be found and fixed before they may be used against the system [2]. Two popular methods for finding weaknesses in software systems are code review and penetration testing [3]. Which method is better for identifying vulnerabilities, nevertheless, is not widely agreed upon [4]. The usefulness of code reviews and penetration tests in locating vulnerabilities is reviewed in detail in this study. We evaluate much empirical research [5] and contrast the benefits and drawbacks of each method. According to our research, both code reviews and penetration tests are useful for uncovering vulnerabilities [6], despite the fact that their effectiveness varies based on the kind of vulnerability, the complexity of the code, and the testers' or reviewers' experience [7][8]. Additionally, we discovered that doing both penetration testing and code review together may be more efficient than using each approach alone [9]. These results may help software engineers, security experts, and researchers choose and use the right approach for locating weaknesses in software systems.

Software defects prediction by metaheuristics tuned extreme gradient boosting and analysis based on Shapley Additive Explanations

Software testing represents a crucial component of software development, and it is usually making the difference between successful and failed projects. Although it is extremely important, due to the fast pace and short deadlines of contemporary projects it is often neglected or not detailed enough due to the lack of available time, leading to the potential loss of reputation, private users’ data, money, and even lives in some circumstances. In such situations, it would be vital to have the option to predict what modules are error-prone according to the collection of software metrics, and to focus testing on them, and that task is a typical classification task. Machine learning models have been frequently employed within a wide range of classification problems with significant success, and this paper proposes eXtreme gradient boosting (XGBoost) model to execute the defect prediction task. A modified variant of the well-known reptile search optimization algorithm has been suggested to carry out the calibrating of the XGBoost hyperparameters. The enhanced algorithm was named HARSA and evaluated on the collection of challenging CEC2019 benchmark functions, where it exhibited excellent performance. Later, the introduced XGBoost model that uses the proposed algorithm has been evaluated on two benchmark software testing datasets, and the simulation outcomes have been compared to other powerful swarm intelligence metaheuristics that were used in the identical experimental environment, where the proposed approach attained superior classification accuracy on both datasets. Finally, Shapley Additive Explanations analysis was conducted to discover the impact of various software metrics on the classification results.

Exploratory Testing: A Dynamic Approach to Uncover Hidden Software Defects

Exploratory testing is a dynamic and iterative method to software testing that emphasizes concurrent learning, test design, and execution. Exploratory testing, as opposed to typical scripted testing approaches, prioritizes tester expertise and creativity in order to identify hidden software faults. This paper delves further into the concept of exploratory testing, including its foundations, benefits, and obstacles. It also explores recommended practices for adopting exploratory testing in software development processes and emphasizes its significance in improving software quality. Organizations can enhance their software quality and find faults that might otherwise go undetected by using this dynamic technique.

Diversity based multi-cluster over sampling approach to alleviate the class imbalance problem in software defect prediction

Diversity based multi-cluster over sampling approach to alleviate the class imbalance problem in software defect prediction

Redefining software reliability modeling: embracing fault-dependency, imperfect removal, and maximum fault considerations

Software reliability is a critical aspect of ensuring the quality and dependability of software systems. However, existing software reliability models often make assumptions that do not align with real-world scenarios, such as perfect fault removal and independent faults. In this paper, we address this gap by developing a software reliability model that considers fault-dependent detection, imperfect fault removal, and the maximum number of faults that may present in the system. By accounting for these factors, our proposed model aims to provide a more accurate representation of software reliability. We evaluate the effectiveness of our model by comparing it to existing models using three commonly used goodness-of-fit criteria. The results demonstrate the importance of incorporating these considerations in software reliability modeling and highlight the superiority of our approach in capturing the complexities associated with software faults. Additionally, this paper conducts an analysis of optimal release planning, which yields highly encouraging results for software managers and engineers. This analysis adds significant value to the existing literature, further emphasizing the practical relevance of our proposed model.

SMOTE-Based Homogeneous Prediction for Aging-Related Bugs in Cloud-Oriented Software

Software aging is the process caused by Aging-Related Bugs (ARBs) which leads to the depletion of resources and degradation of performance in the long run. ARBs are difficult to find and replicate in future studies as they are less in number, thus prediction of ARB is necessary to save cost and time in the testing phase. ARBs are present in low proportion as compared to non-ARBs known as the class Imbalance problem resulting in insufficient training dataset for prediction models. In this study, Synthetic Minority Oversampling Technique (SMOTE) is applied along with homogeneous cross-project ARB prediction to reduce the effect of imbalance problem in software. SMOTE is oversampling of the minority instances synthetically to balance the dataset and improve the capability of defect prediction models. Homogeneous cross-project prediction is implemented where the datasets are different but the distribution of metric sets of both training and testing datasets is similar. The experiment is conducted on five cloud-oriented software such as Cassandra, Hive, Storm, Hadoop HDFS and Hadoop Mapreduce. The novelty of this study is the combination of SMOTE and homogeneous cross-project defect prediction for ARBs in cloud-oriented software. The comparative analysis is also conducted to understand the difference between SMOTE and non-SMOTE results with the help of machine learning classifiers. The result conveys that SMOTE is an efficient method to address class imbalance problem in ARB prediction.

Performance evaluation of NHPP software reliability model applying Gamma family lifetime distribution

In this paper, after applying the Gamma family distribution (Erlang, Log-Logistic, Rayleigh) to the finite failure-type NHPP reliability model in which failures do not occur during flaw repair, the reliability performance of the software was evaluated by comparing with the Goel-Okumoto basic model. In this process, software failure time data collected randomly during the operation of the computer system was used, and the estimator solution of the applied NHPP model was performed by applying the numerical analysis (bisection method).Conclusively, first, as a result of calculating the criteria for efficient model selection, it was evaluated that the efficiency of the Rayleigh and Goel-Okumoto basic models was excellent. Second, as a result of analyzing the reliability attributes functions, the Rayleigh model showed the best performance. Third, as a result of future reliability testing, the Rayleigh model with the highest reliability was efficient. Thus, the Rayleigh model showed the best reliability performance in this work. Through this study, the software reliability performance of the Gamma family lifetime distribution was newly identified, and this analysis data can help software developers to utilize it as basic data for reliability improvement in the testing process.

Integrating programming errors into knowledge graphs for automated assignment of programming tasks

Integrating programming errors into knowledge graphs for automated assignment of programming tasks

Detecting Programming Flaws in Student Submissions with Static Source Code Analysis

"Static code analyzer tools can detect several programming mistakes, that would lead to run-time errors. Such tools can also detect violations of the conventions and guidelines of the given programming language. Thus, the feedback provided by these tools can be valuable for both students and instructors in computer science education. In our paper, we evaluated over 5000 student submissions from the last two years written in C++ and C# programming languages at Eotvos Lorand University Faculty of Informatics (Budapest, Hungary), by executing various static code analyzers on them. From the findings of the analyzers, we highlight some of the most typical and serious issues. Based on these results, we argue to include static analysis of programming submissions in automated and assisted semi-automatic evaluating and grading systems at universities, as these could increase the quality of programming assignments and raise the attention of students on various otherwise missed bugs and other programming errors. 2010 Mathematics Subject Classification. 68U99, 68Q55, 97Q70. 1998 CR Categories and Descriptors. F.3.2 [Theory of Computation]: Logics and Meanings of Programs – Semantics of Programming Languages; D.3.4 [Software]: Programming Languages – Processors; K.3.2 [Computing Milieux]: Computers and Education – Computer and Information Science Education. Key words and phrases. static code analysis, C++, C#, student submission, computer science education, programming flaw."

A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction

Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of methods: feature selection and feature extraction. However, the two categories of methods have their own advantages and limitation. In this paper, we propose a Hybrid Feature Dimensionality Reduction Approach (HFDRA) for SDP, which combines the two different kinds of methods, to improve the performance of SDP. HFDRA approach can be divided into two stages: feature selection and feature extraction. First, HFDRA divides the original features into several feature subsets through a clustering algorithm in the feature selection stage. Then, in the feature extraction stage, kernel principal component analysis (KPCA) is used to reduce the dimensionality of each feature subset. Finally, the reduced-dimensional data is used to build the prediction model. In the empirical study, we use 22 projects from AEEEM, SOFTLAB, MORP, and ReLink as experiment object. In this paper, we first compare our approach with seven baseline methods and three state-of-the-art methods. Then, we analyze the relationship between FR and prediction performance. Experiment results show that our approach outperforms the state-of-the-art data dimensionality reduction methods for defect prediction.

Software Fault Tolerance in Real-Time Systems: Identifying the Future Research Questions

Tolerating hardware faults in modern architectures is becoming a prominent problem due to the miniaturization of the hardware components, their increasing complexity, and the necessity to reduce costs. Software-Implemented Hardware Fault Tolerance approaches have been developed to improve system dependability regarding hardware faults without resorting to custom hardware solutions. However, these come at the expense of making the satisfaction of the timing constraints of the applications/activities harder from a scheduling standpoint. This article surveys the current state-of-the-art of fault tolerance approaches when used in the context of real-time systems, identifying the main challenges and the cross-links between these two topics. We propose a joint scheduling-failure analysis model that highlights the formal interactions among software fault tolerance mechanisms and timing properties. This model allows us to present and discuss many open research questions with the final aim to spur future research activities.

Study on Software Defect Prediction Based on SVM and Decision Tree Algorithm

Software Defect Prediction is a process of identifying the potential defects in software systems before they occur. In this approach, the dataset containing information about software attributes is used as input, and the output is the prediction of whether the software is defective or not. The input dataset is generally in the form of a CSV file, which contains various software attributes such as cyclomatic complexity, essential complexity, Design Complexity, etc. The output of the defect prediction is binary classification. It is done by using SVM (Support Vector Machine) and a decision tree algorithm. This approach can help software developers identify their systems' defects before they cause any harm or affect the system’s performance.

Structuring meaningful bug‐fixing patches to fix software defect

AbstractCurrently, software projects require a significant amount of time, effort and other resources to be invested in software testing to reduce the number of code defects. However, this process decreases the efficiency of software development and leads to a significant waste of workforce and resources. To address this challenge, researchers developed various solutions utilising deep neural networks. However, these solutions are frequently challenged by issues, such as a vast vocabulary, network training difficulties and elongated training processes resulting from the handling of redundant information. To overcome these limitations, the authors proposed a new neural network‐based model named HopFix, designed to detect software defects that may be introduced during the coding process. HopFix consists of four parts: data preprocessing, encoder, decoder and code generation components, which were used for preprocessing data, extracting information about software defects, analysing defect information, generating software patches and controlling the generation process of software patches, respectively. Experimental studies on Bug‐Fix Pairs (BFP) show that HopFix correctly fixed 47.2% (BFPsmall datasets) and 25.7% (BFPmedium datasets) of software defects.

How Does the Corrected Exhalyzer Software Change the Predictive Value of LCI in Pulmonary Exacerbations in Children with Cystic Fibrosis?

Aim: Recently, the most commonly used for multiple breath washout device, the Exhalyzer D, has been shown to overestimate lung clearance index (LCI) results due to a software error. Our study aimed to compare the predictive values of LCI in the CF pulmonary exacerbations (PE) calculated with the updated (3.3.1) and the previous (3.2.1) version of the Spiroware software. Materials and Methods: The measurements were performed during 259 visits in CF pediatric patients. We used 39ΔPE pairs (PE preceded by stable visit) and 138ΔS pairs (stable visit preceded by stable visit) to compare the LCI changes during PE. The areas under the receiver operating curves (AUCROC) and odds ratios were calculated based on the differences between ΔPEs and ΔSs. The exacerbation risk was estimated using a logistic regression model with generalized estimating equations (GEE). Results: There were statistically significant differences in LCI 2.5% median values measured using the two versions of the software in the stable condition but not during PE. The AUCROC for changes between the two consecutive visits for LCI did not change significantly using the updated Spiroware software. Conclusions: Despite the lower median values, using the recalculated LCI values does not influence the diagnostic accuracy of this parameter in CF PE.

SOFTWARE DEFECT PREDICTION USING THE MACHINE LEARNING METHODS

Reliability of software systems is one of the main indicators of quality. Defects occurring when developing software systems have a direct effect on reliability. Precise prediction of defects in software systems helps software engineers to ensure the reliability of software systems and to properly allocate resources for the trial process. The development of an ensemble method by combining several classification methods occupies one of the main places in research conducted in the field of error prediction in software modules. This paper proposes a method based on the application of ensemble training for defect detection. Here, a database obtained from PROMISE and GITHUB software engineering registry is used to detect defects. Experiments are conducted using Weka software. The prediction efficiency is evaluated based on F-measure and ROC-area. As a result of experiments, the defect detection accuracy of the proposed method is proven to be higher than that of individual machine learning methods.

Evaluation of intraoperative neuromonitoring (IONM) data with the Mainz IONM Quality Assurance and Analysis tool

Abstract Background Intraoperative neuromonitoring is widely used in thyroid and parathyroid surgery to prevent unilateral and especially bilateral recurrent nerve paresis. Reference values for amplitude and latency for the recurrent laryngeal nerve and vagus nerve have been published. However, data quality measures that exclude errors of the underlying intraoperative neuromonitoring (IONM) data (immanent software errors, false data labelling) before statistical analysis have not yet been implemented. Methods The authors developed an easy-to-use application (the Mainz IONM Quality Assurance and Analysis tool) using the programming language R. This tool allows visualization, automated and manual correction, and statistical analysis of complete raw data sets (electromyogram signals of all stimulations) from intermittent and continuous neuromonitoring in thyroid and parathyroid surgery. The Mainz IONM Quality Assurance and Analysis tool was used to evaluate IONM data generated and exported from ‘C2’ and ‘C2 Xplore’ neuromonitoring devices (inomed Medizintechnik GmbH) after surgery. For the first time, reference values for latency and amplitude were calculated based on ‘cleaned’ IONM data. Results Intraoperative neuromonitoring data files of 1935 patients consecutively operated on from June 2014 to May 2020 were included. Of 1921 readable files, 34 were excluded for missing data labelling. Automated plausibility checks revealed: less than 3 per cent device errors for electromyogram signal detection; 1138 files (approximately 60 per cent) contained potential labelling errors or inconsistencies necessitating manual review; and 915 files (48.5 per cent) were indeed erroneous. Mean(s.d.) reference onset latencies for the left vagus nerve, right vagus nerve, recurrent laryngeal nerve, and external branch of the superior laryngeal nerve were 6.8(1.1), 4.2(0.8), 2.5(1.1), and 2.1(0.5) ms, respectively. Conclusion Due to high error frequencies, IONM data should undergo in-depth review and multi-step cleaning processes before analysis to standardize scientific reporting. Device software calculates latencies differently; therefore reference values are device-specific (latency) and/or set-up-specific (amplitude). Novel C2-specific reference values for latency and amplitude deviate considerably from published values.

A Software Verification Method for the Internet of Things and Cyber-Physical Systems

With the proliferation of the Internet of Things devices and cyber-physical systems, there is a growing demand for highly functional and high-quality software. To address this demand, it is crucial to employ effective software verification methods. The proposed method is based on the use of physical quantities defined by the International System of Units, which have specific physical dimensions. Additionally, a transformation of the physical value orientation introduced by Siano is utilized. To evaluate the effectiveness of this method, specialized software defect models have been developed. These models are based on the statistical characteristics of the open-source C/C++ code used in drone applications. The advantages of the proposed method include early detection of software defects during compile-time, reduced testing duration, cost savings by identifying a significant portion of latent defects, improved software quality by enhancing reliability, robustness, and performance, as well as complementing existing verification techniques by focusing on latent defects based on software characteristics. By implementing this method, significant reductions in testing time and improvements in both reliability and software quality can be achieved. The method aims to detect 90% of incorrect uses of software variables and over 50% of incorrect uses of operations at both compile-time and run-time.

Analysis Of Phonological Errors In The Program "Lapor Pak!"

This research aims to describe phonological errors in the television program "Lapor Pak!" on the private television station Trans 7. This study is a qualitative research with a qualitative descriptive method. The data source of this research includes all utterances delivered by the hosts, invited guests, or speakers in the program "Lapor Pak!". The data collection technique used in this study is observation and note-taking. The analysis technique employed in this research is qualitative data analysis, which involves identifying, classifying, analyzing, and drawing conclusions from the research findings. The researcher found nine phonological errors, namely metathesis, monophthongization, apheresis, neutralization, zeroization, assimilation, vowel modification, epenthesis, and syncope.