Software Fault Research Articles

ULTRA-Effective labeling of tandem repeats in genomic sequence

Abstract In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally-repetitive sequence, ULTRA (ULTRA Locates Tandemly Repetitive Areas). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions. It is released under an open license, and is available for download at https://github.com/TravisWheelerLab/ULTRA

Computing Precise Control Interface Specifications

Verifying network programs is challenging because of how they divide labor: the control plane computes high level routes through the network and compiles them to device configurations, while the data plane uses these configurations to realize the desired forwarding behavior. In practice, the correctness of the data plane often assumes that the configurations generated by the control plane will satisfy complex specifications. Consequently, validation tools such as program verifiers, runtime monitors, fuzzers, and test-case generators must be aware of these control interface specifications (ci-specs) to avoid raising false alarms. In this paper, we propose the first algorithm for computing precise ci-specs for network data planes. Our specifications are designed to be efficiently monitorable —concretely, checking that a fixed configuration satisfies a ci-spec can be done in polynomial time. Our algorithm, based on modular program instrumentation, quantifier elimination, and a path-based analysis, is more expressive than prior work, and is applicable to practical network programs. We describe an implementation and show that ci-specs computed by our tool are useful for finding real bugs in real-world data plane programs.

CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming

Color programmers manipulate lights, materials, and the resulting colors from light-material interactions. Existing libraries for color programming provide only a thin layer of abstraction around matrix operations. Color programs are, thus, vulnerable to bugs arising from mathematically permissible but physically meaningless matrix computations. Correct implementations are difficult to write and optimize. We introduce CoolerSpace to facilitate physically correct and computationally efficient color programming. CoolerSpace raises the level of abstraction of color programming by allowing programmers to focus on describing the logic of color physics. Correctness and efficiency are handled by CoolerSpace. The type system in CoolerSpace assigns physical meaning and dimensions to user-defined objects. The typing rules permit only legal computations informed by color physics and perception. Along with type checking, CoolerSpace also generates performance-optimized programs using equality saturation. CoolerSpace is implemented as a Python library and compiles to ONNX, a common intermediate representation for tensor computations. CoolerSpace not only prevents common errors in color programming, but also does so without run-time overhead: even unoptimized CoolerSpace programs out-perform existing Python-based color programming systems by up to 5.7 times; our optimizations provide up to an additional 1.4 times speed-up.

The Method of Diversity-Based Ensuring the Reliability of the Router in the IIoT System

The architecture of the Industrial Internet of Things (IIoT) system assumes the presence of a router as the main subsystem, which will allow connecting the subsystems into a single network and connecting them to the equipment of the Internet service provider. Failure of the router can lead to downtime of industrial equipment and the whole workshop at the industrial enterprise, and to the failure of the IIoT system as a whole. Control processing units of industrial routers (RCPU) are complex microprocessor systems (MS). Violation of their operability can be caused by both hardware (HW) and software (SW) failures. Therefore, to increase the reliability of RCPU it is reasonable to use such methods of reliability assurance as HW redundancy (duplication of processors with comparison of results), as well as the use of modern means of control and diagnostics. However, the number of HW and SW failures in RCPUs, is still quite high. In this connection, the solution of issues related to the development of methods for ensuring high reliability of RCPU is of great urgency. This paper proposes a method of ensuring the reliability of RCPU using SW diversity on the basis of priority series. The method let to choose the variant of redundancy of HW and the number of versions of SW as diversity in RCPU.

Information needs in bug reports for web applications

Given the widespread popularity and increasing reliance on long-lived web applications (such as Netflix and Facebook), effective and efficient bug reproduction is essential to maintain functionality and user satisfaction throughout the application’s lifetime. Developers use bug reports to localize, reproduce, and eventually fix software bugs. However, the content of bug reports is not always helpful (e.g., due to incomplete or missing information). In this study, we explore what type of information is often missing in bug reports and how that information is presented in them. We manually analyzed the initial and final versions of 1000 bug reports from 10 popular open-source web-based applications. The analysis revealed that, regardless of the type of software (e.g., e-commerce software or personal tools), diagnostic suggestions from developers and end-user usage information are often missing in initial bug reports but only added later throughout the lifetime of a bug report. Also, textual descriptions and screenshots are used most to describe bugs, regardless of the type of bug (e.g., a functional or performance error). The study highlighted the need for improved bug reporting templates and tools to improve bug report quality and efficiency in web application development and maintenance.

Symbolic testing of floating-point bugs and exceptions

Numerical software are susceptible to floating-point bugs and exceptions, which may lead to severe threats like denial of service attacks. Static analysis techniques such as symbolic execution are effective in detecting general bugs which often cause memory error or program crash. Unfortunately, these methods do not deal well with numerical code as they do not support floating-point constraints and math functions symbolically. In this paper, we propose a new analysis framework YUSE, which can detect floating-point bugs by constructing constraints and exploring paths which contain floating-point expressions. Specifically, we introduce interval computation and interval constraint propagation in non-relational numerical abstract domains, and symbolically model math functions, to accurately detect floating-point bugs and exceptions. Moreover, we leverage two-phase constraint solving to enhance YUSE’s performance. Experimental results show that YUSE outperforms two state-of-the-art tools, Frama-c and Fpse-study, in terms of effectiveness and efficiency, with 1.4× and 7.1× faster than Frama-c and Fpse-study, respectively. Moreover, YUSE found 20 new bugs in real-world software, 12 of which were assigned CVE IDs and 8 of which were confirmed by developers.

Fuzzing drones for anomaly detection: A systematic literature review

Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one commonly used technique for finding bugs and vulnerabilities in software programs and protocols. This article reviews various approaches where fuzzing is applied to detect bugs and vulnerabilities in UAVs. Our goal is to assess the current state-of-the-art fuzzing approaches for UAVs, which are yet to be explored in the literature. We identified open challenges that call for further research to improve the current state-of-the-art.

Online cross-project approach with project-level similarity for just-in-time software defect prediction

Online cross-project approach with project-level similarity for just-in-time software defect prediction

An efficient instance selection algorithm for fast training of support vector machine for cross-project software defect prediction pairs

SVM is limited in its use for cross-project software defect prediction because of its very slow training process. So, this research article proposes a new instance selection (IS) algorithm called boundary detection among classes (BDAC) to reduce the training dataset size for faster training of SVM without degrading the prediction performance. The proposed algorithm is evaluated against six existing IS algorithms based on accuracy, running time, data reduction rate, etc. using 23 general datasets, 18 software defect prediction datasets, and two shape-based datasets, and results prove that BDAC is better than the selected algorithm based on collective comparison.

Component-based modeling of cascading failure propagation in directed dual-weight software networks

Software vulnerabilities often lead to cascading failures, resulting in service unavailability and potential breaches of user data. However, existing models for cascading failure propagation typically focus solely on the static design’s calling relationships, disregarding dynamic runtime propagation paths. Moreover, current network topology models primarily consider function calling frequency while overlooking critical factors like internal failure probability and component failure tolerance rates. Yet, these factors significantly influence the actual propagation of software cascading failures. In this study, we address these limitations by incorporating internal failure probabilities and calling frequencies as node and edge weights, respectively. This forms the basis of our component-based directed dual-weight software network cascading failure propagation model. This model encompasses the evaluation of cascading failure propagation through intra-component and inter-component propagation probabilities, alongside the constraint of component failure tolerance rates. Through extensive experiments conducted on six real-world software applications, our model has demonstrated its effectiveness in predicting software cascading failure propagation processes. This method deepens our understanding of software failures and structures, equipping software testers with the knowledge to make well-informed judgments regarding software quality concerns.

A lightweight RDMA connection protocol based on post-hoc confirmation

With the increasing scale and complexity of high-performance computing systems, the rising failure rate poses significant challenges for RDMA networks that aim for high bandwidth and low latency. RDMA networks require hardware-level end-to-end reliable data transmission services to avoid the high cost of software failure recovery. Tianhe HPC interconnection network adopts a NIC-based RDMA reliable connection protocol, RCP. RCP establishes a connection for each message that enters the NIC and releases it after the transmission is complete. However, this introduces an additional round-trip time RTT connection overhead for each message, which severely impacts the performance of networks dominated by short messages in high-performance computing systems. We have found that utilization of receiver-side connection resources has been consistently low because maintaining message-grained connections on the NIC results in rapid release of connections. Therefore, we propose a lightweight RDMA connection protocol based on post-hoc confirmation, PCP. PCP assumes the receiver has connection resources by default and eliminates the need for confirmation from the receiver before sending a message, thus reducing the connection overhead of almost all messages by one RTT. At the same time, PCP also includes mechanisms to address the special case where the receiver lacks connection resources. Evaluation results demonstrate that PCP significantly optimizes short messages and applications dominated by short messages. Moreover, PCP further reduces the usage of receiver-side connection resources. Additionally, PCP does not experience performance degradation even under large-scale heavy loads and severe endpoint congestion.

Implementation of Artificial Intelligence on Personnel Management in a Dynamic Society

The bedrock of this study is Implementation of Artificial Intelligence (AI) on Personnel Management in a Dynamic Society.. The rapid development and advancement of the digital environment, the increase in computing power, the processing of huge amounts of data and advanced algorithms have not only made AI exceptional and indispensable, but also emerged as a key tool in talent management. Methodologically, the study used secondary data collection sources such as textbooks, magazines, newspapers, journals, and periodical reports. The study found, among other things, that the development of AI leads to errors caused by machines due to programming errors and persistence of discrimination/preferential treatment in recruitment. Therefore, AI is not able to recognize corporate culture and values when evaluating applicants. The paper recommends that companies should focus on updating and training their current employees to avoid job losses due to the emergence of AI and adapt to the current framework conditions, etc. Thus, the paper concludes that the increasing use of AI will have a social and economic impact in this millennium, as it aims at digitalization and development by utilizing AI-inspired decision-making and integrating advanced information systems into multidimensional areas of activity, with a significant impact on various sectors around the world.

Analysis of ERP Based Framework Expert Opinion in Early Software Defect Prediction

Abstract: The software industry necessitates early prediction of software defects for effective quality assessment and resource allocation. During the initial stages of the software development life cycle (SDLC), failure data is often unavailable. Consequently, the insights of domain experts can be crucial in estimating potential software defects during these early phases. This paper introduces a model designed to forecast software defects prior to the testing phase, emphasizing the structure of the software development process. The model is developed using metrics derived from early artifacts of the SDLC. The development and experimental aspects of the model are presented through the application of a Bayesian belief network (BBN). The qualitative aspects of software metrics, along with expert opinions, form the core of this methodology. To demonstrate the practicality and effectiveness of the proposed approach, ten datasets from real software projects have been utilized. The analysis and validation of predicted software defects, based on varying levels of uncertainty from domain experts, are compared against actual defect occurrences.

Prediction of a software reliability model considering the finiteness of software faults

Prediction of a software reliability model considering the finiteness of software faults

Enhancing Software Defect Prediction with Ensemble Models based on Defect Relations Rule Learning

Enhancing Software Defect Prediction with Ensemble Models based on Defect Relations Rule Learning

A Framework for Developing Correct Software Programs through Software Defect Prediction and Elimination using Machine Learning Models

A Framework for Developing Correct Software Programs through Software Defect Prediction and Elimination using Machine Learning Models

LITERATURE REVIEW: ADVANTAGES AND DISADVANTAGES OF BLACK BOX AND WHITE BOX TESTING METHODS

Software testing methods play a crucial role in ensuring the quality, security, and performance of applications. The two main approaches often used are black box and white box testing. This article presents a comprehensive literature review of the characteristics, advantages and disadvantages of both testing methods with the aim of providing in-depth insights for practitioners and researchers in the field of software engineering. The research method used is Systematic Literature Review (SLR), which is a technique of collecting information through the process of searching for literature based on research questions, filtering search results, evaluating, and analyzing relevant research from various academic database sources to gain a comprehensive understanding. Through the analysis of various literature studies, the results of this research are the characteristics of each testing method that is more effectively used in best practice to achieve efficient testing with the coverage of literature review sources in the range of 2020 to 2024. The review of these characteristics is based on seven criteria, namely: test focus, test base, tester's point of view, test case design, program error identification method, program code knowledge, and test effectiveness. In addition, this article presents a comparison of the two methods so that the differences in testing methods can be clearly recognised in terms of how they are implemented. Based on the results of review literature, both methods are better used together in order to ensure that the functional and internal code structure of the software is functioning properly.

Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition

ABSTRACTAccurate software reliability prediction is significant to software quality assurance. However, the rapid development and evolution of modern software pose more challenges for accurate software quality assessment. With the rapid development of machine learning and intelligent algorithms, data‐driven nonparametric models have gradually attracted increasing attention with outstanding prediction performance. However, we found that there may exist some prediction lags caused by the autocorrelation and nonstationarity of software fault data in the prediction of nonparametric models affecting their performance. To address this problem, we proposed an adaptive gated recurrent unit‐based encoder‐decoder model (ED‐GRU) with ensemble empirical mode decomposition (EEMD), effectively reducing prediction lags and performing accurate software fault number prediction. The autocorrelation and nonstationarity of fault data are first reduced by using first‐order difference and EEMD to clearly characterize the changing trend of the data. The frequency‐specific ED‐GRU networks are then combined to adaptively learn the nonlinear fluctuation trend of fault data under different frequency scales and obtain accurate prediction final results after aggregation. Experiments on eight public datasets showed that the proposed EEMD‐ED‐GRU‐PF model could effectively reduce the prediction lags and achieve the best prediction performance compared with four nonparametric and five parametric baseline methods in all datasets. Therefore, the proposed method can effectively and stably reduce the prediction lag to significantly improve the prediction accuracy. In this way, developers can accurately evaluate the current quality of the software and provide valuable guidance for software development and maintenance.

Developing a novel anticipated inherent risk assessment approach applicable in early stages of process design

Inherently Safer Design (ISD), as one of the most fundamental approaches to process safety, focuses on the innate design features. Numerous tools have been developed to assess ISD and risk-based indices are one of the most elevated of them. Existing indices are limited in modelling consequences or are challenging to adapt in terms of probability. This study introduces the Anticipated Inherent Risk Index (AIRI), a robust tool for rapid risk assessment of the worst anticipated scenarios. It incorporates Furthest Effect Distance of the release from the largest connection, by software, and Representative Failure frequency, which is a relative dataset calculated from renown databases. This presents a relative risk map upon major consequences and their likelihood. AIRI applied to a PDH Fractionation Subunits shows that Heat Pump Compressor Suction Drum (AIRI≈3399) in PP Splitter Section, followed by Propylene Trim Condenser (AIRI≈1931) and PP Splitter Reboiler/Condenser (AIRI≈1798) are the equipment with the highest risk. AIRI is a competent tool for early high-level analysis of equipment risk, which gives useful indications on design and how to improve it. This tool also has the potential to be equipped with vaster frequency datasets and effects analysis and to be incorporated into other process safety studies.

Software Defect Prediction Based on Machine Learning and Deep Learning Techniques: An Empirical Approach

Software Defect Prediction Based on Machine Learning and Deep Learning Techniques: An Empirical Approach