Software Fault Research Articles

#2519 Technical complications assessment of the novel SILENCIA© APD cycler

Abstract Background and Aims A new gravimetric peritoneal dialysis (PD) cycler, SILENCIA (Fresenius Medical Care, Bad Homburg, Germany), has been reported to achieve not only an adequate dialysis dose, but it is proposed also to be easy to use, cost-effective, and silent. In this study, we explored the alarms, cautions and number of terminated treatments due to potential errors and malfunctions of this new cycler. Method Over a period of 6 months, technical related device data was collected remotely, via a cloud-based infrastructure, including a Gateway and a Card Reader. The technical related data was then analyzed and visualized in a Dashboard. In special occasions, e.g., in case of device issues additional further technical data was collected on-site at patients’ home by a Service Technician. Results In total, 1860 technical related device data sets were collected in 3 different countries (Chile, Ecuador, and Turkey) from the treatments of 18 PD patients. 15 out of 32 patients were between 31 and 50 years old, and the mean PD duration was 2.14 years (n = 32). In total 9 alarms (5 per 1000 treatments) occurred, 5 being related to software bugs, 4 undetermined. 99.5% of the treatments were performed without alarms. In average, 5.3 cautions per 1000 treatments were observed, 94.7% due to blocked tubing system. Power failures only occurred in 4% of the treatments, with an average duration of 38.6 minutes. During power failures, the device does not wake up patients, proceeds as the power is back and treatment stays within schedule due to FlexPoint. A detailed list of alarms, cautions and power failures is presented in Table 1. Conclusion The results of this study demonstrate that SILENCIA supports PD treatment with a low number of alarms and cautions, which may help to achieve a smooth therapy for the patients, especially considering that the treatments are mainly performed during the night.

#2507 Enhancing critical care nephrology: analyzing continuous renal replacement therapy with a citrate-based anticoagulation focus

Abstract Background and Aims Continuous renal replacement therapy (CRRT) is pivotal for managing acute kidney injury (AKI) unstable patients. However, questions persist regarding prescription and the role of anticoagulation. This study aims to examine CRRT practices, focus on citrate-based anticoagulation and clarify contraindications. Method This study comprises a retrospective analysis of a patient cohort requiring CRRT and admitted to the Postoperative Intensive Care Unit and Burn Unit. Inclusion criteria involved the need for CRRT and adult age, with exclusion criteria based on treatment duration (<48 hours), reducing the initial cohort from 256 to 176 patients. Clinical variables, including gender, age, underlying pathology, CRRT modality, and anticoagulation type, were characterized. Subsequently, the association between anticoagulation type and clot formation within 48 hours and in-hospital mortality was investigated. Furthermore, an analysis of citrate-based CRRT was conducted, evaluating laboratory values (Table 1) at the beginning and 48 hours using the Wilcoxon test, and their correlation with clot development and mortality. Additionally, we used a logistic regression model to explore the relationship between AST, ALT, and lactate values with citrate accumulation. Finally, a survival analysis was conducted. Results The sample consisted of 66.5% males and 33.5% females, with an average age of 59.48 years. Reasons for admission are depicted in Fig. 1. CRRT modalities were 20.5% HDVVC, 76.1% HDFVVC, and 3.4% HFVVC. Anticoagulation included 35.2% none, 28.4% heparin, 33.5% citrate, and 2.8% alprostadil. Filter clotting occurred in 29.1% withing 48 hours. Heparin was associated with a 5.4-fold increased risk of clotting compared to citrate. Mortality was 63.3%, with no anticoagulation type showing a significant association. Parameters at CRRT initiation and 48 hours for citrate-based CRRT patients and Wilcoxon test results are presented in Table 1. Only 5.1% had filter clotting <48 hours, and no baseline parameters were significantly associated with an increased clotting risk. There was one case of citrate intoxication due to a programming error, which was not included. 76.81% had CaT/CaI <2.1, and 7.25% had CaT/CaI >2.1, requiring technique adjustments. Aminotransferases and lactate values, often considered as contraindications for citrate, were analyzed: The average AST and ALT values were 188.53 IU/l and 108.08 IU/l, respectively. When AST levels exceeded 5 times the normal range and ALT levels exceeded 3 times the normal range, a statistically significant association with CaT/CaI >2.1 at the 48-hour mark of CRRT utilization was observed (p < 0.001 for both).The average lactic acid value was 1.74 mg/dl. Having a lactic acid level >4 was associated (p < 0.05) with a higher risk of developing CaT/CaI >2.1. 54.2% of the sample died. No baseline parameters were significantly associated with it. At 48 hours, higher bicarbonate levels and metabolic alkalosis were associated with increased mortality (p < 0.05). The median survival time was 11 days (±4.38). Conclusion This study supports the use of citrate-based anticoagulation in CRRT, as no cases of citrate intoxication (CaT/CaI >2.5) were found. Nonetheless, a significantly elevated AST (>5 times normal), ALT (>3 times normal), and lactate (>4 mg/dl), were associated with a higher risk of citrate accumulation (CaT/CaI >2.1). Customized approaches and preventive measures to mitigate the development of metabolic alkalosis are essential for optimizing patient outcomes.

Empowering software reliability: Leveraging efficient fault detection and removal efficiency

Advancements in science and technology have led to the widespread use of computer systems in various applications, emphasizing the importance of software reliability. Software failures can have severe consequences, making thorough testing crucial. Software reliability growth models (SRGMs) play a significant role in enhancing reliability by predicting improvement over time. This article introduces a comprehensive approach to software reliability that incorporates a dynamic fault detection rate, along with fault removal efficiency. The fault detection rate measures the rate at which faults are identified during testing, reflecting the effectiveness of the testing process. By incorporating this dynamic component, the model provides a more accurate estimation of software reliability and enables adaptive testing strategies and resource allocation. Achieving a high fault detection rate is desirable, but organizations must consider the cost implications and strike a balance between reliability and time-to-market constraints. This article extends the analysis to calculate the optimal release time and optimal warranty period that minimize development costs, subject to the desired reliability. By considering these factors, development teams can make informed decisions regarding the timing of software release and the duration of the warranty period, optimizing both reliability and cost.

Online boxplot derived outlier detection

AbstractOutlier detection is a widely used technique for identifying anomalous or exceptional events across various contexts. It has proven to be valuable in applications like fault detection, fraud detection, and real-time monitoring systems. Detecting outliers in real time is crucial in several industries, such as financial fraud detection and quality control in manufacturing processes. In the context of big data, the amount of data generated is enormous, and traditional batch mode methods are not practical since the entire dataset is not available. The limited computational resources further compound this issue. Boxplot is a widely used batch mode algorithm for outlier detection that involves several derivations. However, the lack of an incremental closed form for statistical calculations during boxplot construction poses considerable challenges for its application within the realm of big data. We propose an incremental/online version of the boxplot algorithm to address these challenges. Our proposed algorithm is based on an approximation approach that involves numerical integration of the histogram and calculation of the cumulative distribution function. This approach is independent of the dataset’s distribution, making it effective for all types of distributions, whether skewed or not. To assess the efficacy of the proposed algorithm, we conducted tests using simulated datasets featuring varying degrees of skewness. Additionally, we applied the algorithm to a real-world dataset concerning software fault detection, which posed a considerable challenge. The experimental results underscored the robust performance of our proposed algorithm, highlighting its efficacy comparable to batch mode methods that access the entire dataset. Our online boxplot method, leveraging dataset distribution to define whiskers, consistently achieved exceptional outlier detection results. Notably, our algorithm demonstrated computational efficiency, maintaining constant memory usage with minimal hyperparameter tuning.

Nature-inspired approaches in Software Fault Prediction

In software engineering, predicting software faults is a crucial task for ensuring high software quality and reducing costs. In recent years, nature inspired approaches have been increasingly used in software fault prediction. In this paper, we explore the effectiveness of six nature inspired algorithms, namely Ant Colony, Particle Swarm Optimization, Firefly, Bat, Harris Hawks, and Genetic Algorithm, for software fault prediction. We evaluate the algorithms using three commonly used datasets, JM1, CM1, and PC1. Our experimental results show that nature inspired approaches can effectively predict software faults, with some algorithms performing better than others depending on the dataset used. Our findings suggest that these approaches have potential to be used as a practical and efficient means for software fault prediction. Keywords— nature inspired algorithms; PSO; Ant Colony Optimization; Harris Hawks; Genetic Algorithm (GA); python programming; Jupyter Notebook; confusion matrix;

Implementation of a health information technology safety classification system in the Veterans Health Administration's Informatics Patient Safety Office.

Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office. A team of informatics safety analysts retrospectively classified 1year of HIT patient safety issues by type of HIT patient safety concern using consensus discussions. The processes established during retrospective classification were then applied to incoming HIT safety issues moving forward. Of 140 issues retrospectively reviewed, 124 met the classification criteria. The majority were HIT failures (eg, software defects) (33.1%) or configuration and implementation problems (29.8%). Unmet user needs and external system interactions accounted for 20.2% and 10.5%, respectively. Absence of HIT safety features accounted for 2.4% of issues, and 4% did not have enough information to classify. The 5-type HIT safety concern classification framework generated actionable categories helping organizations effectively respond to HIT patient safety risks.

Design of Software Reliability Prediction using Radial Basis Function Networks with Nonlinear Analysis and Topological Considerations

This study presents a thorough methodology for predicting software reliability by employing Radial Basis Function Networks (RBFNs). By using machine learning methodologies such as Radial Basis Function Networks (RBFNs), software development teams are enabled to make well-informed decisions, optimize resource allocation, and proactively mitigate potential dependability concerns. Consequently, this results in improved software quality and heightened customer happiness. This methodology involves multiple stages, starting with data collection and preprocessing. We assemble a comprehensive dataset comprising historical software performance metrics, defect reports, and relevant development process information. After data cleansing and feature engineering, we split the dataset into training, validation, and testing subsets. The core of this approach lies in the construction and training of RBFNs. These neural networks consist of an input layer, a hidden layer with radial basis functions, and an output layer. The architecture parameters, particularly the number of hidden neurons and the spread parameter of the radial basis functions, are optimized through a systematic hyperparameter tuning process using the validation dataset. Upon achieving an optimal model configuration, we rigorously evaluate the RBFN predictive capabilities using the testing dataset.

Understanding the Impact of Changes in Application Characteristics on SRGM

Due to the increasing reliance on technology in nearly every industry over the past three decades, it has become necessary to evaluate the performance of a software product prior to its formal release in the market. The properties of a software application, such as its complexity and lines of code, are subject to change over time as a result of factors namely the testing environment, allocation of resources, testing efficiency, and testing team’s expertise. The assumption of constant Fault Detection Rate (FDR) may not accurately anticipate the potential number of bugs correctly. Keeping all these considerations in mind, a framework is developed to incorporate change point in the development of a testing effort-based Software Reliability Growth Model (SRGM) that takes into account the effect of application characteristics under both perfect and imperfect debugging settings. In addition, these outcomes are compared to the model without a change point. The proposed model is validated on two real-life software fault datasets. The results demonstrate that the proposed model performs better than the model without a change point.

Software defect prediction ensemble learning algorithm based on 2-step sparrow optimizing extreme learning machine

Software defect prediction ensemble learning algorithm based on 2-step sparrow optimizing extreme learning machine

Software Defect Prediction and Automatic Repair Based on Machine Learning

Software defects can have a significant impact on quality, reliability, and development costs if not addressed properly. With the increasing complexity of modern software, traditional testing methods struggle to keep up. Machine learning and deep learning have emerged as promising new approaches for software defect prediction (SDP) and automatic repair. These approaches leverage historical data to identify patterns associated with defects. Deep learning models can automatically learn complex feature representations from raw code. However, imbalanced defect data can bias models, necessitating techniques such as data augmentation or cost-sensitive learning. Memory leaks are a common defect that can gradually deplete resources if left unrepaired. Long short-term memory (LSTM) networks are well-suited for sequential code analysis and can exploit long-term dependencies to learn complex patterns and identify leak scenarios. This research aims to advance SDP and repair through machine learning techniques.

Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets

Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets

A clustering-based resampling technique with cluster structure analysis for software defect detection in imbalanced datasets

Software defect detection focuses on the automatic identification of flaws in software modules. Given the great importance of the problem, numerous researchers have introduced a rich collection of deep learning approaches to confront it. However, the datasets that are used to train the proposed classifiers are in most cases highly imbalanced, leading to models that cannot learn the minority classes effectively, while being biased towards the majority class. The state-of-the-art solutions either overlook the issue of data imbalance, or they confront it insufficiently by ignoring the existence of outliers and the local properties of the classes' distributions. In this work we introduce CBR, a Clustering-Based Resampling technique for mitigating the problem of class imbalance in software defect detection tasks. The proposed method initially employs a quite simple heuristic to determine the maximum distance threshold between two clusters. Then, it uses this threshold to apply hierarchical clustering with the aim of grouping together similar samples. CBR considers the singleton clusters as outliers, and discards the ones originating from the majority class. The algorithm subsequently organizes the clusters into sub-clusters than contain samples from the same class and determines which sub-clusters should participate in the oversampling process. In this way, CBR produces samples of improved quality and variance. We evaluated the performance of CBR against 9 baseline and state-of-the-art techniques by using 27 datasets and a Multilayer Perceptron classifier. The results demonstrate the superiority of CBR in terms of Balanced Accuracy and Precision scores.

Leveraging Distributed Systems for Fault-Tolerant Cloud Computing: A Review of Strategies and Frameworks

Ensuring system availability and reliability is crucial in the quickly developing field of cloud computing. The importance of fault tolerance in cloud infrastructure systems grows as organizations become more reliant on it to support their critical operations. The purpose of this article is to investigate the intricate realm of cloud computing and distributed systems. Specifically, the paper will investigate the numerous forms of cloud computing, fault tolerance methods, and frameworks that enable cloud services to be robust and durable. Cloud computing has transformed the way in which organizations and individuals access and administer computing resources. The paper discusses several deployment options, including public, private, hybrid, and multi-cloud environments, which provide organizations with the advantages of flexibility, scalability, and cost-effectiveness. The inherent flexibility of cloud computing renders it well-suited for a diverse range of applications, spanning from the hosting of websites to the execution of intricate data analytics processes. Generally, cloud computing encounters substantial obstacles, including the need of maintaining uninterrupted service in the face of hardware failures, network outages, or software errors, despite its tremendous benefits. The critical importance of fault tolerance in this particular situation cannot be overstated, as it plays a pivotal role in maintaining the dependability and availability of the system. The primary objective of this study is to examine the utilization of distributed systems as a means to augment fault tolerance within the realm of cloud computing and distributed systems. Distributed systems offer an optimal approach for addressing difficulties related to fault tolerance, owing to its intrinsic capability to divide workloads and data over several nodes. This approach utilizes redundancy, replication, and the ability to recover seamlessly from disturbances, hence enhancing the resilience and resource efficiency of cloud services. This research reviews novel techniques and frameworks that utilize distributed systems to create fault-tolerant cloud computing architectures, emphasizing their substantial influence on the cloud computing domain. In conclusion, this research report includes a comparative analysis table that encompasses twenty preceding works.

An Improved and Optimized Random Forest Based Approach to Predict the Software Faults

Effective software fault prediction is crucial for minimizing errors during software development and preventing subsequent failures. This research introduces an enhanced Random Forest-based approach for predicting software faults, specifically focusing on the NASA JM1 dataset. The dataset comprises 21 software metrics indicating the presence or absence of faults in a module, and it is utilized to evaluate the proposed approach. The study delves into the intricacies of the NASA dataset, detailing the cleaning process and addressing class imbalance through Synthetic Minority Over-sampling Technique (SMOTE). The core of our approach involves the implementation and fine-tuning of the Random Forest classifier, with a specific focus on optimizing hyperparameters to enhance predictive accuracy. In comparative evaluations with standard machine learning models, our proposed approach demonstrated superior performance, achieving an accuracy of 82.96% and an F1 score of 89.53%. Notably, we emphasize the significance of software defects and their potential to cause failures and crashes during software development, leading to substantial organizational losses. The paper provides a comprehensive examination of different aspects of the machine learning model, offering detailed insights, examples, and illustrative figures to enhance the understanding of our proposed approach.

A Neural Network Approach for Software Reliability Prediction

The increasing reliance on computer software has raised significant concerns regarding software reliability evaluation. Over the past four decades, various software reliability models have been developed, encompassing both parametric and nonparametric approaches. However, no single model has demonstrated effectiveness in handling all types of datasets. In response to this challenge, the deep neural network (DNN), a powerful deep learning model, has emerged as a promising solution. By leveraging the flexibility and adaptability of artificial neural networks (ANN), the DNN model exhibits remarkable prediction performance by capturing training variables and exploring deeper layers. This study presents an approach that utilizes a DNN model based on an ANN architecture with multiple activation functions. This approach aims to estimate software reliability and predict the frequency of software flaws. Through extensive experimental analysis and validation, the proposed DNN model surpasses the predictive accuracy achieved by existing parametric and nonparametric models. These results highlight the potential of the DNN model in effectively addressing the complexities involved in software reliability evaluation. By combining the power of deep learning with the adaptability of ANN, the proposed model offers a promising and accurate solution for software reliability prediction.

Evaluation of physical activity before and after respiratory rehabilitation in normal weight individuals with asthma: a feasibility study.

Individuals with asthma spend less time engaging in physical activity compared to the general population. Increasing physical activity has become a patient-centered goal for the treatment of treatable traits of individuals with asthma. There are data showing the possible effects of a pulmonary rehabilitation program on physical activity in obese individuals with asthma but not in normal-weight asthmatics. The objective of this feasibility study is to estimate the number of daily steps and time spent on activity in normal-weight individuals with asthma, measured before and after a pulmonary rehabilitation program. Normal-weight individuals with moderate to severe asthma were evaluated. The individuals measured their daily steps with an accelerometer for 5 days before and after a pulmonary rehabilitation program. The study was registered on ClinicalTrials.gov: NCT05486689. In total, 17 participants were enrolled; one dropout and data on the time in activity of two individuals are missing due to a software error during the download. Data from 16 patients were analyzed. The median number of steps/day at baseline was 5,578 (25th, 75th percentiles = 4,874, 9,685) while the median activity time was 214 min (25th, 75th percentiles = 165, 239). After the rehabilitation program, the number of daily steps increased by a median value of 472 (p-value = 0.561) and the time in activity reduced by 17 min (p-value = 0.357). We also found a significant difference in quality of life, muscle strength, and exercise capacity. The results of this study make it possible to calculate the sample size of future studies whose main outcome is daily steps in normal-weight individuals with asthma. The difficulties encountered in downloading time in activity data do not allow the same for this outcome. ClinicalTrials.gov, identifier NCT05486689.

A new binary chaos-based metaheuristic algorithm for software defect prediction

Software defect prediction is a critical challenge within software engineering aimed at enhancing software quality by proactively identifying potential defects. This approach involves selecting defect-prone modules ahead of the testing phase, thereby reducing testing time and costs. Machine learning methods provide developers with valuable models for categorising faulty software modules. However, the challenge arises from the numerous elements present in the training dataset, which frequently reduce the accuracy and precision of classification. Addressing this, selecting effective features for classification from the dataset becomes an NP-hard problem, often tackled using metaheuristic algorithms. This study introduces a novel approach, the Binary Chaos-based Olympiad Optimisation Algorithm, specifically designed to select the most impactful features from the training dataset. By selecting these influential features for classification, the precision and accuracy of software module classifiers can be notably improved. The study's primary contributions involve devising a binary variant of the chaos-based Olympiad optimisation algorithm to meticulously select effective features and construct an efficient classification model for identifying faulty software modules. Five real-world and standard datasets were utilised across both the training and testing phases of the classifier to evaluate the proposed method's effectiveness. The findings highlight that among the 21 features within the training datasets, specific metrics such as basic complexity, the sum of operators and operands, lines of code, quantity of lines containing code and comments, and the sum of operands have the most significant influence on software defect prediction. This research underscores the combined effectiveness of the proposed method and machine learning algorithms, significantly boosting accuracy (91.13%), precision (92.74%), recall (97.61%), and F1 score (94.26%) in software defect prediction.

Rand-Index Target Projective Gradient Deep Belief Network for Software Fault Prediction

Predicting defective software modules before testing is a valuable operation that reduces time and cost of software testing. Source code fault prediction plays a vital role in improving software quality that effectively assists in optimization testing resource allocation. Several machine learning and ensemble learning techniques has been extensively evolved over the recent few years to predict defect at an early stage. These techniques made predictions based on historical defect data, the software metrics. Nevertheless, the time efficient and accurate fault predictions are the major challenging tasks that yet have to be addressed. In order to ensure accurate software fault prediction, a method called Rand-Index Target Projective Gradient Deep Belief Network (RTPGDBN) is designed. The proposed RTPGDBN method comprises of three processes namely acquiring JAVA packages, software metric selection and classification. First, the number of JAVA packages is used as input from the dataset. Second with the JAVA packages obtained as input, Rand similarity indexive target projection function is applied for selecting the most significant software metrics in order to minimize time complexity of fault prediction. Third, with the selected metrics, classification is performed using Tversky Gradient Deep Belief neural network. Also, gradient descent function is applied to minimize classification error, therefore ensuring accurate software fault classification results obtained at the output layer. Experimental setup of proposed RPGDDBN and existing methods are implemented in Java language and the dataset collected from smell prediction replication package. Performance analysis is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity and space complexity. Through extensive experiments on repository data, experimental results indicate that our RPGDDBN method outperforms two state-of-the-art defect detection methods in terms of different performance metrics.

A novel STPA approach to software safety and security in autonomous maritime systems

With higher autonomy in maritime systems, tasks and responsibilities are moved from the human operator to software, increasing the complexity and the importance of safe and reliable functionality. Software failures, however, may be introduced from the early life cycle phases intentionally or unintentionally, and these must therefore be mitigated by safe and secure design approaches. A challenge is that existing methods are not particularly well-suited for analyzing software risks. Thus, the objective of this paper is to propose a systematic and efficient software failure identification approach by extending the Systems-Theoretic Process Analysis (STPA) with a software failure taxonomy and the System Modeling Language (SysML). This enables the control structure in STPA to cover both the dynamic and static aspects of the software functions. Combined with an implementation platform independent questionnaire, this gives a more systematic and guided search for potential software failures than existing approaches. To demonstrate the proposed approach, a case study on a ferry's navigation system that operates in manual control or semi-autonomous mode is performed. In the case study, the focus is on creating an avoidance map data structure, including both moving and static obstacles to be avoided by the ferry, and the subsequent process of collision risk warning calculation. Software failures are identified and evaluated in collision scenarios where the ferry operates under foggy conditions. The paper shows that the proposed systematic approach provides an improved process for identifying and analyzing critical software failures. This facilitates enhanced risk mitigation in the design and testing phases contributing to autonomous systems' safety and security.

Depth linear discrimination-oriented feature selection method based on adaptive sine cosine algorithm for software defect prediction

Software Defect Prediction (SDP) plays a vital role in the software development life cycle as it helps identify and fix software defects. However, predicting software defects with irrelevant features and overlapping classes is challenging and can lead to lengthy training and low model accuracy. To address these challenges, this research introduces a novel Depth Linear Discrimination-Oriented Feature Selection Method based on Adaptive Sine Cosine Algorithm, named Depth Adaptive Sine Cosine Feature Selection (DASC-FS). DASC-FS integrates the Adaptive Sine Cosine Algorithm (ASCA) as a search algorithm to determine the relevant features and adopts Depth Linear Discriminant Analysis (D-LDA) to identify the discriminative features that maximize class separation. The paper proposes ASCA which is a metaheuristic algorithm meticulously designed to enhance the search capabilities of the standard Sine Cosine Algorithm (SCA). Combining the simplicity of the SCA with the efficiency of multiple mutation operators inspired by Genetic Algorithms (GA), ASCA enhances the diversity of the solutions and imparts remarkable adaptability to various situations. Furthermore, this study introduces a novel linear discriminant method, called Depth Linear Discriminant Analysis (D-LDA) to enhance the robustness of the original LDA. D-LDA systematically integrates the matrix depth concept into LDA, offering a systematic approach to address the challenges associated with scatter matrix estimation. As matrix depth measures how central or deep a particular matrix is within a distribution with respect to different directions, it is an efficient tool for computing a robust scatter matrix estimator that can handle outliers and complex data structures. The experimental results showed that DASC-FS consistently obtains the highest accuracy compared to most existing methods by integrating ASCA and D-LDA, thereby considering both accuracy optimization and class separation. The results also show that the use of multiple mutation operators in ASCA improves the search process capabilities. The results also show that the capacity of D-LDA to reduce data dimensionality and increase class separation yields highly competitive results compared to other LDAs. Finally, features related to code size and complexity have emerged as key factors for SDP because they consistently rank as important features across different classifiers and datasets. DASC-FS offers a valuable solution in domain knowledge for enhancing predictive accuracy and understanding factors contributing to software defects through enhanced search capabilities, robust scatter matrix estimation, and the ability to reduce data dimensionality.