Software Fault Research Articles

Empirical evaluation of the performance of data sampling and feature selection techniques for software fault prediction

Context:The application of Software Fault Prediction (SFP) in the software development life cycle to predict the faulty class at the early stage has piqued the interest of various scholars. In the SFP domain, during research analysis, it got realized that there has been very little work instigated on addressing both class imbalance and feature redundancy problems jointly to enhance the performance and prediction accuracy of SFP models. It has been perceived in the literature survey the study of droughts with the comprehensive comparative analysis of different sampling and feature selection strategies together. Objective:This research builds an extensive assessment of distinct combinations of different feature selection and sampling approaches, to effectively overcome the problems of class overlap, class imbalance, and feature redundancy. The objective is to determine the best combination that will produce results with a higher degree of accuracy and an effective SFP model. Method:Considering the above erudition, the study has applied 8 different sampling techniques along with 10 feature selection algorithms against 56 open-source projects. The comparative analysis is performed against 5346 variants of input datasets by applying 8 different classifiers to predict the faulty class. In addition, the research paper presents an intensive assessment and performance of these techniques individually against all the input projects. We have considered accuracy and Area Under the ROC (receiver operating characteristic curve) Curve (AUC) performance metrics to compare the performance of different models developed using the classification algorithm. Result:For each project in the proposed work, we evaluated a total of 792 combinations that were produced using 10 feature selection methods, 1 all metrics dataset, 8 sampling methods, 1 original, unsampled dataset, and 8 classifiers. The empirical result indicates that, against 21 projects out of 54 projects, Synthetic Minority Over Sampling Technique Edited (SMOTEE) with correlation-based feature selection (FS2) combination outperformed with the highest AUC value which is 38.89 % of projects. Additionally, according to experimental results, the highest AUC values were attained by 24.07 % of projects using the SMOTEE, FS2, and RF combination. Conclusion:The results of the statical analysis test reveal that 93.42 % of the combinational pairs of different sampling and feature selection approaches demonstrated a significant variance in the performance of the distinct combinations of sampling and feature selection techniques. The empirical result indicates the performance of the SFP Model is adversely impacted by class imbalance and irrelevance. The outcome indicates for more than 75% of projects, the performance of trained models improved with an AUC value between a range of 0.805 to 0.99 post-application of sampling and feature selection strategies, in comparison without the use of feature selection and sampling techniques.

Explaining software fault predictions to spreadsheet users

A variety of automated software fault prediction techniques was proposed in recent years, in particular for the important class of spreadsheet programs. Software fault prediction techniques commonly create ranked lists of “suspicious” program statements for developers to inspect. Existing research, however, suggests that solely providing such ranked lists may not always be effective. In particular, it was found that developers often seek for explanations for the outcomes provided by a debugging tool and that such explanations may be key for developers to trust and rely on the tool. Research on how to explain the outcomes of fault prediction techniques, which are often based on complex machine learning models, is scarce, and little is known regarding how such explanations are perceived by developers. With this work, we aim to narrow this research gap and study the perception of different forms of explanations by spreadsheet users in the context of a machine learning based fault prediction tool. A between-subjects user study (N=120) revealed significant differences between the explored explanation styles. In particular, we found that well-designed natural language explanations can indeed help users better understand why certain spreadsheet cells were marked by the debugging tool and that such explanations can be effective to increase the users’ trust compared to a black box system.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

A research landscape on software defect prediction

AbstractSoftware defect prediction is the process of identifying defective files and modules that need rigorous testing. In the literature, several secondary studies including systematic reviews, mapping studies, and review studies have been reported. However, no research work such as a tertiary study that combines secondary studies has focused on providing a landscape of software defect prediction useful to understand the body of knowledge. Motivated by this, we intend to perform a tertiary study by following a systematic literature review protocol to provide a research landscape of the targeted domain. We synthesize the quality of the secondary studies and investigate the employed techniques and the performance evaluation measures for evaluating the software defect prediction model. Furthermore, this study aims at exploring different datasets employed in the reported experimentation. Moreover, the current study intends at highlighting the research trends, gaps, and opportunities in the targeted research domain. The results indicate that none of the reported defect prediction techniques can be regarded as the best; however, the reported techniques performed better in different testing situations. In addition, machine learning (ML)‐based techniques perform better than traditional statistical techniques mainly due to the potential of discovering the defects and generating generalized results. Moreover, the obtained results highlight the need for further work in the domain of ML‐based techniques. Furthermore, publicly available datasets should be considered for experimentation or replication purposes. The potential future work can focus on data quality, ethical ML, cross‐project defect prediction, early defect prediction process, class imbalance problem, and model overfitting.

ADA-PC: An asynchronous distributed algorithm for minimizing pairwise connectivity in wireless multi-hop networks

Reliability analysis is of great significance to designing and maintaining wireless multi-hop networks (WMhNs). In WMhNs, several reasons can cause a node to be inoperable, such as hardware failure, software errors, and battery drain. Failure of some critical nodes may partition networks into disconnected segments. The presence of such critical nodes may also reduce the network lifetime since they consume more energy for packet forwarding. Therefore, it is crucial to identify the critical nodes of the networks and strengthen them by adding more nodes surrounding those or creating alternate pathways connecting other nodes to ensure connectivity maintenance in WMhNs. One of the most common approaches in direction is detecting the cut nodes of the networks. However, although finding cut nodes provide helpful information, it may be insufficient for precise reliability analysis since finding cut nodes only does not consider the remaining network. Critical Node Problem (CNP) aims to detect the most important nodes of the network whose removal minimizes the pairwise connectivity (the total number of node pairs connected by at least one path). In other words, the CNP tries to identify a set of nodes whose absence partitions the network into several disconnected segments of similar size. Detecting critical nodes for pairwise connectivity reveals the weak points and bottlenecks of the networks and may help to increase the fault tolerance and lifetime of WMhNs. This paper proposes an Asynchronous Distributed Algorithm for minimizing Pairwise Connectivity (ADA-PC) in WMhNs. To the best of our knowledge, this is the first distributed algorithm for the targeted problem in the network literature. The proposed algorithm uses a distributed Breadth-First Search (BFS) tree which limits bit complexity to O(n.m.log2n) and space complexity to O(d), where d is the network’s diameter. The experimental study on both testbed experiment and simulation reveals that the proposed algorithm is capable of finding the most critical nodes with up to 60% lower sent bytes than the existing central algorithms.

A soft computing approach for software defect density prediction

AbstractDefect density is an essential software testing and maintenance aspect that determines the quality of software products. It is used as a management factor to distribute limited human resources successfully. The availability of public defect datasets facilitates building defect density prediction models using established static code metrics. Since the data gathered for software modules are often subject to uncertainty, it becomes difficult to deliver accurate and reliable predictions. To alleviate this issue, we propose a new prediction model that integrates gray system theory and fuzzy logic to handle the imprecision in software measurement. We propose a new similarity measure that combines the benefits of fuzzy logic and gray relational analysis. The proposed model was validated against defect density prediction models using public defect datasets. The defect density variable is frequently sparse because of the vast number of none‐defected modules in the datasets. Therefore, we also check our proposed model's performance against the sparsity level. The findings reveal that the developed model surpasses other defect density prediction models over the datasets with high and very high sparsity ratios. The ensemble learning techniques are competitive choices to the proposed model when the sparsity ratio is relatively small. On the other hand, the statistical regression models were the most inadequate methods for such problems and datasets. Finally, the proposed model was evaluated against different degrees of uncertainty using a sensitivity analysis procedure. The results showed that our model behaves stably under different degrees of uncertainty.

On a cost and availability analysis for software systems via phase type non-homogeneous Poisson process

To build highly reliable software products, software testing is required. The software engineer has a problem determining whether to complete the testing process and when to release the software system to the market. As the focus on high software increases, project managers must consider the cost of testing, testing availability time, and release time strategy. This research article utilizes a phase type non-homogeneous Poisson process to investigate the cost analysis and operational availability of a software reliability model. The software failure inter-arrival time is considered to follow a phase type distribution. The phase type software reliability model is valuable in reducing the time and effort required to select the appropriate models for software reliability evaluation. An explicit expression for the expected, bi-criterion cost analysis and operational availability is derived using standard results of the renewal reward theorem. The optimal software release policy T* is obtained analytically. Numerical illustrations and sensitivity analysis have been presented to illustrate the cost and availability analysis’s efficiency and demonstrate conformity with the study’s observations.

A Survey on FPGA Cybersecurity Design Strategies

This article presents a critical literature review on the security aspects of field-programmable gate array (FPGA) devices. FPGA devices present unique challenges to cybersecurity through their reconfigurable nature. The article also pays special attention to emerging system-on-chip (SoC) FPGA devices that incorporate a hard processing system (HPS) on the same die as the FPGA logic. While this incorporation reduces the need for vulnerable external signals, the HPS in SoC FPGA devices adds a level of complexity that is not present for stand-alone FPGA devices. This added complexity necessarily hands over the task of securing the device to developers. Even with standard security features in place, the HPS might still have unhindered access to the FPGA logic. A single software flaw could open up a breach that might allow an attacker to extract the FPGA’s configuration data. A robust cybersecurity strategy is thus required for developers. As such, this work aims to provide the groundwork to build a solid threat-based cybersecurity design strategy that is specially adapted to SoC FPGA devices.

The Impact of Smart Pump Technology in the Healthcare System: A Scope Review.

Smart infusion pump technology prevents errors caused by parenteral therapy. This paper aims to review the recent literature about smart pump uses, cases and adverse events, and strategies to minimize these disadvantages. Literature was explored from January 2000 to November 2021 using Google Scholar, PubMed, and ScienceDirect. There were assessments of the advantages and adverse effects of using smart pumps and strategies to overcome the adverse effects of smart pumps. The advantage of using smart pumps is that they decrease errors like incorrect rate and dose. Other benefits include a decrease in medication event rates and the ability to connect smart pumps to home health providers. However, compliance rates were negatively influenced by improper smart pumps and the overriding of soft alerts, which can cause alert fatigue and drug library update delays. Recent studies have tried to address the negative issues by improving drug library compliance and decreasing alerts to avoid alert desensitization. The investigations revealed that the smart pumps reduced errors but would only prevent some programming errors. Compliance with utilizing smart pump technology is critical in stopping medication errors. Opportunities for future improvement are broad, including integrating a smart pump infusion with the hospital system, implementing auto programming, and designing smart pump devices to be lighter, smaller, and more portable instead of the heavy, large smart pump used by most hospitals today.

An intuitionistic fuzzy representation based software bug severity prediction approach for imbalanced severity classes

In order to improve software reliability and quality, the triager must assess the severity of the software bug and allocate suitable resources on time. However, the triager faces many difficulties in understanding various software bugs that involve lots of uncertainty and irregularities. Additionally, it can be challenging for the triager to determine the severity of bugs that are semantically close to multiple severity labels. To address these problems, a topic modeling and intuitionistic fuzzy similarity measure-based software bug severity prediction technique (IFSBSP) is proposed in this paper. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is applied to balance the severity classes in software bug repositories. Then topic modeling is used to generate topics based on the probability of underlying uncertainty in software bugs. Using these topics, the intuitionistic fuzzy membership, non-membership, and hesitancy membership degrees of a software bug are calculated for multiple severity labels. Then, 15 IFS techniques are investigated for a new bug in order to compute its similarity to multiple severity labels. The Eclipse, Mozilla, Apache, and NetBeans software bug repositories are used to evaluate the performance of IFSBSP and the state-of-the-art models. On these software bug repositories, the IFSBSP model outperforms state-of-the-art models by achieving accuracy of 91.6%, 90.9%, 88.1%, and 92.9% and an F-measure of 90.7%, 91.1%, 89.3%, and 91.7%, respectively.

Multi-Objective Optimization Model and Hierarchical Attention Networks for Mutation Testing

Mutation testing is devised for measuring test suite adequacy by identifying the artificially induced faults in software. This paper presents a novel approach by considering multiobjectives-based optimization. Here, the optimal test suite generation is performed using the proposed water cycle water wave optimization (WCWWO). The best test suites are generated by satisfying the multi-objective factors, such as time of execution, test suite size, mutant score, and mutant reduction rate. The WCWWO is devised by a combination of the water cycle algorithm (WCA) and water wave optimization (WWO). The hierarchical attention network (HAN) is used for classifying the equivalent mutants by utilizing the MutPy tool. Furthermore, the performance of the developed WCWWO+HAN is evaluated in terms of three metrics—mutant score (MS), mutant reduction rate (MRR), and fitness—with the maximal MS of 0.585, higher MRR of 0.397, and maximum fitness of 0.652.

Data sampling approach using heuristic Learning Vector Quantization (LVQ) classifier for software defect prediction

On the basis of quality estimate, early prediction and identification of software flaws is crucial in the software area. Prediction of Software Defects SDP is defined as the process of exposing software to flaws through the use of prediction models and defect datasets. This study recommended a method for dealing with the class imbalance problem based on Improved Random Synthetic Minority Oversampling Technique (SMOTE), followed by Linear Pearson Correlation Technique to perform feature selection to predict software failure. On the basis of the SMOTE data sampling approach, a strategy for software defect prediction is given in this paper. To address the class imbalance, the defect datasets were initially processed using the Improved Random-SMOTE Oversampling technique. Then, using the Linear Pearson Correlation approach, the features were chosen, and using the k-fold cross validation process, the samples were split into training and testing datasets. Finally, Heuristic Learning Vector Quantization is used to classify data in order to predict software problems. Based on measures like sensitivity, specificity, FPR, and accuracy rate for two separate datasets, the performance of the proposed strategy is contrasted with the approaches to classification that presently exist.

False Alarm Reduction Method for Weakness Static Analysis Using BERT Model

In the era of the fourth Industrial Revolution, software has recently been applied in many fields. As the size and complexity of software increase, security attack problems continue to arise owing to potential software defects, resulting in significant social losses. To reduce software defects, a secure software development life cycle (SDLC) should be systematically developed and managed. In particular, a software weakness analyzer that uses a static analysis tool to check software weaknesses at the time of development is a very effective tool for solving software weaknesses. However, because numerous false alarms can be reported even when they are not real weaknesses, programmers and reviewers must review them, resulting in a decrease in the productivity of development. In this study, we present a system that uses the BERT model to determine the reliability of the weakness analysis results generated by the static analysis tool and to reduce false alarms by reclassifying the derived results into a decision tree model. Thus, it is possible to maintain the advantages of static analysis tools and increase productivity by reducing the cost of program development and the review process.

Design of intelligent module design for humanoid translation robot by combining the deep learning with blockchain technology

To accelerate the deep application of deep learning in text data processing, an English statistical translation system is established and applied to the question answering of humanoid robot. Firstly, the model of machine translation based on recursive neural network is implemented. A crawler system is established to collect English movie subtitle data. On this basis, an English subtitle translation system is designed. Then, combined with sentence embedding technology, the Particle Swarm Optimization (PSO) algorithm of meta-heuristic algorithm is adopted to locate the defects of translation software. A translation robot automatic question and answer interactive module is constructed. Additionally, the hybrid recommendation mechanism based on personalized learning is built using blockchain technology. Finally, the performance of translation model and software defect location model is evaluated. The results show that the Recurrent Neural Network (RNN) embedding algorithm has certain effect of word clustering. RNN embedded model has a strong ability to process short sentences. The strongest translated sentences are between 11 and 39 words long, while the weakest translated sentences are between 71 and 79 words long. Therefore, the model must strengthen the processing of long sentences, especially character—level input. The average sentence length is much longer than word-level input. The model based on PSO algorithm shows good accuracy in different data sets. This model averages better performance on Tomcat, standard widget toolkits, and Java development tool datasets than other comparison methods. The average reciprocal rank and average accuracy of the weight combination of PSO algorithm are very high. Moreover, this method is greatly affected by the dimension of the word embedding model, and the 300-dimension word embedding model has the best effect. To sum up, this study proposes a good statistical translation model for humanoid robot English translation, which lays the foundation for intelligent interaction between humanoid robots.

Developing Conceptual Framework of Software Defect Prediction in Software Testing: The Case of Ethiopian Software Industries

Purpose: Software defect prediction is one of the most active research areas in software engineering. As our dependency on software is increasing, software quality is becoming gradually more and more important in present era. Software used almost everywhere and in every tread of life. Software consequences such as fault and failures may diminish the quality of software which leads to customer dissatisfaction. Different points that are related to the current work had been discussed.
 Methodology: This research contains a detailed explanation of the scientific methods and methodologies used for the design of proposed framework. Primary and secondary source of data has been used.
 Findings: Due to the tremendous amount of data generated daily from fields such as business that software has resulted in the generation of tremendous amount of defected data. As the organizations struggle to handle and utilize effectively all the information available in order to provide better products and services and gain competitive advantage, defect separation and the so called “big data” analytics are two fields that currently constitute matter of concern and discussion.
 Unique Contribution to Theory, Practice and Policy: There are different research areas in defect prediction using big data analytics defect segmentations one of the issues. Defect Segmentation is an important component of much organization, and the algorithm designed in this work is expected to be a significant contribution to the field, and mainly to researchers working on various aspects of defect prediction defect segmentation using big data analytics. Therefore, the researchers in the area can use the algorithm or the implemented system for processing segmentation as component in their research, mainly on machine learning applications.

Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction

Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction

An annotation-based approach for finding bugs in neural network programs

As neural networks are increasingly included as core components of safety–critical systems, developing effective testing techniques specialized for them becomes crucial. The bulk of the research has focused on testing neural-network models; but these models are defined by writing programs, and there is growing evidence that these neural-network programs often have bugs too.This paper presents aNNoTest: an approach to generating test inputs for neural-network programs. A fundamental challenge is that the dynamically-typed languages (e.g., Python) commonly used to program neural networks cannot express detailed constraints about valid function inputs (e.g., matrices with certain dimensions). Without knowing these constraints, automated test-case generation is prone to producing invalid inputs, which trigger spurious failures and are useless for identifying real bugs. To address this problem, we introduce a simple annotation language tailored for concisely expressing valid function inputs in neural-network programs. aNNoTest takes as input an annotated program, and uses property-based testing to generate random inputs that satisfy the validity constraints. In the paper, we also outline guidelines that simplify writing aNNoTest annotations.We evaluated aNNoTest on 19 neural-network programs from Islam et al’s survey. Islam et al. (2019), which we manually annotated following our guidelines — producing 6 annotations per tested function on average. aNNoTest automatically generated test inputs that revealed 94 bugs, including 63 bugs that the survey reported for these projects. These results suggest that aNNoTest can be a valuable approach to finding widespread bugs in real-world neural-network programs.

Building a Tool for Optimal Test Cases Selection using Artificial Intelligence Techniques

Software testing is an important process for detecting errors in programs and reducing the risks of their use. With the rapid expansion of the software industry and the heavy dependence on increasingly popular and frequently used programs, there is a necessary need to use software testing techniques that are efficient, scalable, applicable, and effective in detecting errors. In this research, a tool was built that selects the optimal test cases using artificial intelligence techniques. The crow search algorithm was used to select test cases, and after modifications and improvements were made to the algorithm, the improved crow search algorithm was proposed, which generates and selects test cases that achieve the basic paths of the program, depending on the hybridization between the criterion of close to boundary value and branch coverage in calculating the fitness function, and relying on the crow's awareness probability value. In addition, the genetic algorithm was used for test case prioritization.

An Improved Confounding Effect Model for Software Defect Prediction

Software defect prediction technology can effectively improve software quality. Depending on the code metrics, machine learning models are built to predict potential defects. Some researchers have indicated that the size metric could cause confounding effects and bias the prediction results. However, evidence shows that the real confounder should be the development cycle and number of developers, which could bring confounding effects when using code metrics for prediction. This paper proposes an improved confounding effect model, introducing a new confounding variable into the traditional model. On multiple projects, we experimentally analyzed the effect extent of the confounding variable. Furthermore, we verified that controlling confounding variables helps improve the predictive model’s performance.

Developing Conceptual Framework of Software Defect Prediction in Software Testing: The Case of Ethiopian Software Industries

Purpose: Software defect prediction is one of the most active research areas in software engineering. As our dependency on software is increasing, software quality is becoming gradually more and more important in present era. Software used almost everywhere and in every tread of life. Software consequences such as fault and failures may diminish the quality of software which leads to customer dissatisfaction. Different points that are related to the current work had been discussed.
 Methodology: This research contains a detailed explanation of the scientific methods and methodologies used for the design of proposed framework. Primary and secondary source of data has been used.
 Findings: Due to the tremendous amount of data generated daily from fields such as business that software has resulted in the generation of tremendous amount of defected data. As the organizations struggle to handle and utilize effectively all the information available in order to provide better products and services and gain competitive advantage, defect separation and the so called “big data” analytics are two fields that currently constitute matter of concern and discussion.
 Unique Contribution to Theory, Practice and Policy: There are different research areas in defect prediction using big data analytics defect segmentations one of the issues. Defect Segmentation is an important component of much organization, and the algorithm designed in this work is expected to be a significant contribution to the field, and mainly to researchers working on various aspects of defect prediction defect segmentation using big data analytics. Therefore, the researchers in the area can use the algorithm or the implemented system for processing segmentation as component in their research, mainly on machine learning applications.

The role of computer supported project-based learning in students’ computational thinking and engagement in robotics courses

Computational thinking (CT) is a necessary skill for students. It is as important as the other essential skills such as reading, writing, and arithmetic. However, how to improve students’ CT in robotics is still to be explored. Hence, this study examined the effects of computer supported project-based learning (CSPBL) on students’ CT and learning engagement through comparing students’ CT and engagement in two courses instructed by the same instructor (one instructed with a traditional method, the other instructed with CSPBL). Participants were seventy-nine students from a university in China. Questionnaires, interviews, and project analysis were used to measure students’ CT and learning engagement. The comparative results of self-reports and final projects indicated that the CSPBL enhanced students’ CT. The simulation project training based on the SenseStudy learning platform in CSPBL helped students debug the programming code, optimize the algorithm design, and improve their algorithmic thinking, cooperation, and problem solving during the program error correction. The CSPBL also improved students’ engagement in learning, especially for those with more than one year of programming experience. It was found that the CSPBL was effective in terms of improving students’ CT and engagement in robotics learning. This study provides teachers with practical suggestions to conduct effective robotics instruction.