Software Fault Research Articles

Defect Prediction via Tree-Based Encoding with Hybrid Granularity for Software Sustainability

Defects in software may result in system crashes, sluggish performance, or even deadlock, leading to the depletion of valuable resources. Implementing defect prediction can assist quality assurance teams in identifying potential software issues and rationalizing the allocation of testing resources, thereby decreasing the elimination of resources and enhancing software sustainability. Researchers have recently incorporated deep learning into defect prediction, extracting structural-semantic features from codes' abstract syntax trees (ASTs). However, inappropriate node granularity in ASTs may adversely impact the effectiveness of the extracted features. In addition, converting AST nodes into integer vectors may lead to the loss of structure information, resulting in poor model predictive capability. This paper proposes a tree-based encoding method with hybrid granularity for defect prediction to address these challenges. Specifically, five granularity selection schemes are extended to generate various ASTs from codes. Subsequently, a tree-based continuous bag-of-words model is utilized to map nodes of ASTs into numeric vector representations that conform to the tree-like structure of codes. The matrices converted from ASTs are then fed into a convolutional neural network to extract program features automatically. Experiments involving 24 versions of open-source projects demonstrate that our method can improve the effectiveness of extracted features in defect prediction tasks.

Theory and evidence-base for a digital platform for the delivery of language tests during awake craniotomy and collaborative brain mapping

ObjectivesBuild the theoretical and evidence-base for a digital platform (map-OR) which delivers intraoperative language tests during awake craniotomy and facilitates collaborative sharing of brain mapping data.DesignMixed methodology study including two...

Optimizing software fault prediction using decision tree regression and soft computing techniques

Optimizing software fault prediction using decision tree regression and soft computing techniques

Flight safety assessment based on a modified human error risk quantification approach.

In risk and safety assessments of aviation systems, engineers generally pay more attention to the risks of hardware or software failure and focus less on the risks caused by human errors. In this paper, a (FRAHE) method is proposed for identifying this critical error type and determining the risk severity of human errors. This method accounts for the human error probability as well as the impacts of human errors on the system. The fuzzy inference approach is employed in this paper to address the uncertainty and issues of imprecision that arise from insufficient information and scarce error data and a risk assessment model of human error is developed. The model can be used to precisely describe the relationship between the output risk severity and the input risk indicators, including the human error probability, the error impact probability, and the human error consequence. A case study of the approach task is presented to demonstrate the availability and reasonability of the model. The risk-based modeling method can not only provide valuable information for reducing the occurrence of critical errors but also be used to conduct prospective analyses to prevent unsafe incidents or aviation accidents.

Expanding access to identification cards and social programs: Experimental evidence from Panamá

AbstractWe experimentally analyze the effects of an intervention to induce the renewal of identification cards on access to a government social program in Panamá. On‐time renewals and access to government transfers increased by 10 and 3.6 percentage points, respectively. Simple reminders about expiration dates generated larger effects than also enabling individuals to renew their documents through an online platform, which users struggled to use despite its being designed to simplify the process. The results suggest that increasing access to identity documentation can reduce exclusion errors in social programs and that simply granting access to e‐government platforms may not necessarily improve public service delivery.

Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach

While static analysis is instrumental in uncovering software bugs, its precision in analyzing large and intricate codebases remains challenging. The emerging prowess of Large Language Models (LLMs) offers a promising avenue to address these complexities. In this paper, we present LLift, a pioneering framework that synergizes static analysis and LLMs, with a spotlight on identifying use-before-initialization (UBI) bugs within the Linux kernel. Drawing from our insights into variable usage conventions in Linux, we enhance path analysis using post-constraint guidance. This approach, combined with our methodically crafted procedures, empowers LLift to adeptly handle the challenges of bug-specific modeling, extensive codebases, and the unpredictable nature of LLMs. Our real-world evaluations identified four previously undiscovered UBI bugs in the mainstream Linux kernel, which the Linux community has acknowledged. This study reaffirms the potential of marrying static analysis with LLMs, setting a compelling direction for future research in this area.

Enhancing Security in Industrial Application Development: Case Study on Self-Generating Artificial Intelligence Tools

The emergence of security vulnerabilities and risks in software development assisted by self-generated tools, particularly with regard to the generation of code that lacks due consideration of security measures, could have significant consequences for industry and its organizations. This manuscript aims to demonstrate how such self-generative vulnerabilities manifest in software programming, through a case study. To this end, this work undertakes a methodology that illustrates a practical example of vulnerability existing in the code generated using an AI model such as ChatGPT, showcasing the creation of a web application database, SQL queries, and PHP server-side. At the same time, the experimentation details a step-by-step SQL injection attack process, highlighting the hacker’s actions to exploit the vulnerability in the website’s database structure, through iterative testing and executing SQL commands to gain access to sensitive data. Recommendations on effective prevention strategies include training programs, error analysis, responsible attitude, integration of tools and audits in software development, and collaboration with third parties. As a result, this manuscript discusses compliance with regulatory frameworks such as GDPR and HIPAA, along with the adoption of standards such as ISO/IEC 27002 or ISA/IEC 62443, for industrial applications. Such measures lead to the conclusion that incorporating secure coding standards and guideline—from organizations such as OWASP and CERT training programs—further strengthens defenses against vulnerabilities introduced by AI-generated code and novice programming errors, ultimately improving overall security and regulatory compliance.

ПРОГРАММНО-АЛГОРИТМИЧЕСКОЕ ОБЕСПЕЧЕНИЕ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ ТЕХНИЧЕСКОГО ЗРЕНИЯ ДЛЯ ОПРЕДЕЛЕНИЯ ПАРАМЕТРОВ КОРНЕПЛОДОВ САХАРНОЙ СВЕКЛЫ ПРИ СОРТИРОВКЕ ПЕРЕД УКЛАДКОЙ НА ХРАНЕНИЕ

The application of methods of long-term storage of sugar beet for a period of more than 2 months is justified. It has been found that small root crops are more susceptible to temperature fluctuations and relative humidity of the environment, which leads to a change in the partial pressure of water vapor between the surface of sugar beet and the surrounding air. During storage in a sugar beet mound, the relative humidity reaches from 90 to 95% due to the removal of moisture from the surface of root crops, which is a source of beet mass losses. The criteria for the size of sugar beet root crops have been determined. The necessity of creating a method for sorting trucks at the receiving point of sugar factories is shown. It was revealed that when determining the requirements for sugar beet entering storage, it is necessary to take into account, in addition to physical and chemical parameters, the value of the specific surface area of the root crop. Therefore, it is recommended to send large root crops weighing more than 570 g to kagats. The purpose of the article is formulated, which is to develop software and algorithmic support for determining the parameters of sugar beet root crops when sorting trucks. The process of distribution of trucks at the receiving point of sugar factories by man at the present time is described. A block diagram of the algorithm of the intelligent vision system program is presented and described. The features of the created software are described, such as working with sensors, processing software or hardware errors. According to the results of the study, an algorithm for the operation of the program and software of an intelligent vision system for determining the parameters of sugar beet root crops have been developed [1].

Runtime Analysis of Competitive Co-evolutionary Algorithms for Maximin Optimisation of a Bilinear Function

Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliable. This paper provides a first step in developing runtime analysis for population-based competitive co-evolutionary algorithms. We provide a mathematical framework for describing and reasoning about the performance of co-evolutionary processes. To illustrate the framework, we introduce a population-based co-evolutionary algorithm called PDCoEA, and prove that it obtains a solution to a bilinear maximin optimisation problem in expected polynomial time. Finally, we describe settings where PDCoEA needs exponential time with overwhelmingly high probability to obtain a solution.

Ensemble feature ranking approach for software fault prediction

Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy.

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.