Development Of Reliable Software Research Articles

Transformer-based models application for bug detection in source code

This paper explores the use of transformer-based models for bug detection in source code, aiming to better understand the capacity of these models to learn complex patterns and relationships within the code. Traditional static analysis tools are highly limited in their ability to detect semantic errors, resulting in numerous defects passing through to the code execution stage. This research represents a step towards enhancing static code analysis using neural networks. The experiments were designed as binary classification tasks to detect buggy code snippets, each targeting a specific defect type such as NameError, TypeError, IndexError, AttributeError, ValueError, EOFError, SyntaxError, and ModuleNotFoundError. Utilizing the «RunBugRun» dataset, which relies on code execution results, the models – BERT, CodeBERT, GPT-2, and CodeT5 – were fine-tuned and compared under identical conditions and hyperparameters. Performance was evaluated using F1-Score, Precision, and Recall. The results indicated that transformer-based models, especially CodeT5 and CodeBERT, were effective in identifying various defects, demonstrating their ability to learn complex code patterns. However, performance varied by defect type, with some defects like IndexError and TypeError being more challenging to detect. The outcomes underscore the importance of high-quality, diverse training data and highlight the potential of transformer-based models to achieve more accurate early defect detection. Future research should further explore advanced transformer architectures for detecting complicated defects, and investigate the integration of additional contextual information to the detection process. This study highlights the potential of modern machine learning architectures to advance software engineering practices, leading to more efficient and reliable software development.

Research on software reliability enhancement methods based on iterative development

Iterative development is an agile software development methodology that speeds up the development process and increases the flexibility of software delivery. However, the current software requirements change extremely fast, which leads to its increasing uncertainty, and iterative development also faces the challenge of software reliability. The software reliability enhancement method based on iterative development is researched and discussed for the current situation of insufficient reliability in the traditional software development process. In traditional software development, one-time delivery pressure and overall design complexity often lead to software functional defects and instability, affecting software reliability. The iterative development method, on the other hand, improves software reliability by decomposing the software development process into multiple iterative cycles, each of which contains requirements analysis, design, coding, and testing, updating the software functionality in smaller increments, and gradually improving the software product. This study proposes a software reliability improvement methodology by exploring and analyzing the key factors related to reliability in the iterative development process. This dissertation can establish a reliable and high-quality software development process through the steps of meticulous requirements planning and management, iterative development and testing, automated testing and continuous integration, code quality control, defect management and continuous improvement, user feedback and user engagement, and continuous learning and technology enhancement.

Multi-type requirements traceability prediction by code data augmentation and fine-tuning MS-CodeBERT

Requirement traceability is a crucial quality factor that highly impacts the software evolution process and maintenance costs. Automated traceability links recovery techniques are required for a reliable and low-cost software development life cycle. Pre-trained language models have shown promising results on many natural language tasks. However, using such pre-trained models for requirement traceability needs large and quality traceability datasets and accurate fine-tuning mechanisms. This paper proposes code augmentation and fine-tuning techniques to prepare the MS-CodeBERT pre-trained language model for various types of requirements traceability prediction including documentation-to-method, issue-to-commit, and issue-to-method links. Three program transformation operations, namely, Rename Variable, Swap Operands, and Swap Statements are designed to generate new quality samples increasing the sample diversity of the traceability datasets. A 2-stage and 3-stage fine-tuning mechanism is proposed to fine-tune the language model for the three types of requirement traceability prediction on provided datasets. Experiments on 14 Java projects demonstrate a 6.2% to 8.5% improvement in the precision, 2.5% to 5.2% improvement in the recall, and 3.8% to 7.3% improvement in the F1 score of the traceability prediction models compared to the best results from the state-of-the-art methods.

Advances in Mass Spectrometry of Gangliosides Expressed in Brain Cancers.

Gangliosides are highly abundant in the human brain where they are involved in major biological events. In brain cancers, alterations of ganglioside pattern occur, some of which being correlated with neoplastic transformation, while others with tumor proliferation. Of all techniques, mass spectrometry (MS) has proven to be one of the most effective in gangliosidomics, due to its ability to characterize heterogeneous mixtures and discover species with biomarker value. This review highlights the most significant achievements of MS in the analysis of gangliosides in human brain cancers. The first part presents the latest state of MS development in the discovery of ganglioside markers in primary brain tumors, with a particular emphasis on the ion mobility separation (IMS) MS and its contribution to the elucidation of the gangliosidome associated with aggressive tumors. The second part is focused on MS of gangliosides in brain metastases, highlighting the ability of matrix-assisted laser desorption/ionization (MALDI)-MS, microfluidics-MS and tandem MS to decipher and structurally characterize species involved in the metastatic process. In the end, several conclusions and perspectives are presented, among which the need for development of reliable software and a user-friendly structural database as a search platform in brain tumor diagnostics.

Predicting software defects with swarm-intelligence-based machine learning algorithm for improved process quality

The quality and effectiveness of software systems may be significantly impacted by Software Defects (SD). Therefore, enhancing process quality is essential for controlling and minimizing the incidence of faults. Implementing reliable Software Development (SDe) processes and best practices is one way to do this information. SD, commonly referred to as software bugs or software mistakes, are defects or errors that occur in computer programs and cause them to act up or create unintended outcomes. These vulnerabilities may appear for several causes, including programming mistakes, poor design choices, or issues with the SDe cycle. The prediction of software problems based on Machine Learning (ML) using an Enhanced Artificial Neural Network (E-ANN) is implemented in this study to increase software quality and testing effectiveness. Particle swarm optimization and grey wolf optimization, these two algorithms named grey wolf swarm optimization algorithm (GWSA), are combined to recognize the corresponding compensation of the methods following the respective benefits and drawbacks. The hybrid algorithm-based model to the conventional hyperparameter optimization strategy and a single swarm intelligence algorithm's investigation of investigational consequences from six data sets shows that the hybrid algorithm-based model has high and enhanced indicators. Processed by the autoencoder, the model's performance has also improved.

IoT-based agriculture monitoring platform architecture

The research is aimed at solving one of the most urgent tasks of our time: the development of reliable and scalable software for monitoring the climate of agricultural fields. The goal is to create an architecture of simple and universal systems for collecting and storing data from various measuring devices located on agricultural land. The methodological basis for the monitoring system is the “Internet of Things” (IoT) approach. The agro-industrial complex of the Tomsk region (Russia) was chosen as a model object. About a hundred measuring devices have been installed in agricultural fields, which provide continuous delivery of data series in real time. Our developed platform includes both the server part of the application that processes and stores these weather data, and a convenient user interface for visualizing data to agronomists. The results of the research can be used in designing the architecture of similar IoT-based monitoring systems.

Artificial Intelligence Algorithms for Analysis of Geographic Atrophy: A Review and Evaluation.

PurposeThe purpose of this study was to summarize and evaluate artificial intelligence (AI) algorithms used in geographic atrophy (GA) diagnostic processes (e.g. isolating lesions or disease progression).MethodsThe search strategy and selection of publications were both conducted in accordance with the Preferred of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed and Web of Science were used to extract literary data. The algorithms were summarized by objective, performance, and scope of coverage of GA diagnosis (e.g. lesion automation and GA progression).ResultsTwenty-seven studies were identified for this review. A total of 18 publications focused on lesion segmentation only, 2 were designed to detect and classify GA, 2 were designed to predict future overall GA progression, 3 focused on prediction of future spatial GA progression, and 2 focused on prediction of visual function in GA. GA-related algorithms reported sensitivities from 0.47 to 0.98, specificities from 0.73 to 0.99, accuracies from 0.42 to 0.995, and Dice coefficients from 0.66 to 0.89.ConclusionsCurrent GA-AI publications have a predominant focus on lesion segmentation and a minor focus on classification and progression analysis. AI could be applied to other facets of GA diagnoses, such as understanding the role of hyperfluorescent areas in GA. Using AI for GA has several advantages, including improved diagnostic accuracy and faster processing speeds.Translational RelevanceAI can be used to quantify GA lesions and therefore allows one to impute visual function and quality-of-life. However, there is a need for the development of reliable and objective models and software to predict the rate of GA progression and to quantify improvements due to interventions.

A Smart Security Drones for Farms Using Software Architecture

Predators like lions and elephants, around farms to kill and destroy a farm's livestock. For this reason, productivity in agriculture decreases over time resulting in poor production of crops and even killing of poultry animals. The development drone for driven agriculture based on image process and IoT technology provides an effective way to ensure the protection of farms and agriculture against wild animals. An automatic drone flies after receiving a signal from server with machine learning to hunt wild animals outside the farm. The focus is on object detection which is also the highlight of this project. Furthermore, the UAV drone was developed through UAV technologies, with functionalities necessary for the successful deployment of a fully autonomous UAV operation over agriculture and traffic networks. The UAV is able to navigate autonomously at different altitudes and plan for mission goals such as locating, identifying, tracking, and the development of reliable software and hardware architectures. This article proposes a UML diagram including the design of the unmanned aerial vehicle system and software in it. The software is called embedded software. Model-based testing is a resolution for testing the embedded software.

Programming Theorems and Their Applications

One of the effective methodological approaches in programming that supports the design and development of reliable software is analogy-based programming. Within this framework, the method of problem reduction plays a key role. Reducing a given problem to another one whose solving algorithm is already known can be made more efficient by the application of programming theorems. These represent proven, abstract solutions – in a general form – to some of the most common problems in programming. In this article, we present six fundamental programming theorems as well as pose five sample problems. In solving these problems, all six programming theorems will be applied. In the process of reduction, we will employ a concise specification language. Programming theorems and solutions to the problems will be given using the structogram form. However, we will use pseudocodes as descriptions of algorithms resembling their actual implementation in Python. A functional style solution to one of the problems will also be presented, which is to illustrate that for the implementation in Python, it is sufficient to give the specification of the problem for the design of the solution. The content of the article essentially corresponds to that of the introductory lectures of a course we offered to students enrolled in the Applied Mathematics specialization.
 Subject Classification: D40

An Enhanced Design of Sparse Autoencoder for Latent Features Extraction Based on Trigonometric Simplexes for Network Intrusion Detection Systems

Despite the successful contributions in the field of network intrusion detection using machine learning algorithms and deep networks to learn the boundaries between normal traffic and network attacks, it is still challenging to detect various attacks with high performance. In this paper, we propose a novel mathematical model for further development of robust, reliable, and efficient software for practical intrusion detection applications. In this present work, we are concerned with optimal hyperparameters tuned for high performance sparse autoencoders for optimizing features and classifying normal and abnormal traffic patterns. The proposed framework allows the parameters of the back-propagation learning algorithm to be tuned with respect to the performance and architecture of the sparse autoencoder through a sequence of trigonometric simplex designs. These hyperparameters include the number of nodes in the hidden layer, learning rate of the hidden layer, and learning rate of the output layer. It is expected to achieve better results in extracting features and adapting to various levels of learning hierarchy as different layers of the autoencoder are characterized by different learning rates in the proposed framework. The idea is viewed such that every learning rate of a hidden layer is a dimension in a multidimensional space. Hence, a vector of the adaptive learning rates is implemented for the multiple layers of the network to accelerate the processing time that is required for the network to learn the mapping towards a combination of enhanced features and the optimal synaptic weights in the multiple layers for a given problem. The suggested framework is tested on CICIDS2017, a reliable intrusion detection dataset that covers all the common, updated intrusions and cyber-attacks. Experimental results demonstrate that the proposed architecture for intrusion detection yields superior performance compared to recently published algorithms in terms of classification accuracy and F-measure results.

Improving Software Development effort estimating using Support Vector Regression and Feature Selection

Accurate and reliable software development effort estimation (SDEE) is one of the main concerns for project managers. Planning and scheduling a software projects using and inaccurate estimate may cause severe risks to software project under development such as delayed delivery, poor quality software, missing features. Therefore, accurate prediction of software effort plays an important role in the minimization of these risks that can lead to projects failure. Nowadays, application of artificial intelligence techniques has grown dramatically for predicting software effort. The researchers found that these techniques are suitable tools for accurate prediction. In this study, an improved model is designed for estimating software effort using support vector regression (SVR) and two feature selection (FS) methods. Prior to building model step, a preprocessing stage is performed by random forest or Boruta feature selection methods to remove unimportant features. Next, the SVR model is tuned by a grid search approach. The performance of model is then evaluated over eight well-known datasets through 30%holdout validation method. To show the impact of feature selection on the accuracy of SVR model, the proposed model was compared with SVR model without feature selection. The results indicated that SVR with feature selection outperforms SVR without FS in terms of the three accuracy measures used in this empirical study.

Comparison of 3 Surveillance Methods to Detect Potential Controlled Substance Diversion in an Academic Medical Center.

Objectives: To compare 3 methods of detecting potential diversion of controlled substances (CS) by health care personnel from inpatient units in a large, academic medical center. Methods: Three different reports were retrospectively analyzed and evaluated to determine which employees are "high-risk" for diversion over a 30-day period using defined criteria. Reports were derived from automated dispensing machines (ADMs), purchased third-party software (TPS), and the electronic health record (EHR). The primary outcome was the percentage of employees in each report who were deemed to be high-risk for CS diversion (positive predictive value [PPV]). Secondary outcomes included the number of false positives and description of high-risk users on each report. Descriptive statistics were used to analyze differences between methods. Results: The PPV was highly variable between reports. The PPVs among the ADM, TPS, and EHR reports were 3.28%, 6.82%, and 23.88%, respectively. False positives were high among all reports (96.72%, 93.18%, and 76.12% for the ADM, TPS, and EHR reports, respectively). Conclusions: A report from the EHR has the highest PPV to detect high-risk employees who may be diverting CS. However, false positives were high for all reports, indicating that significant improvements are needed in the development of accurate and reliable software to detect potential and actual CS diversion.

Технология разработки надежного программного обеспечения

Технология разработки надежного программного обеспечения

A framework for genomic sequencing on clusters of multicore and manycore processors

The advances in genomic sequencing during the past few years have motivated the development of fast and reliable software for DNA/RNA sequencing on current high performance architectures. Most of these efforts target multicore processors, only a few can also exploit graphics processing units, and a much smaller set will run in clusters equipped with any of these multi-threaded architecture technologies. Furthermore, the examples that can be used on clusters today are all strongly coupled with a particular aligner. In this paper we introduce an alignment framework that can be leveraged to coordinately run any “single-node” aligner, taking advantage of the resources of a cluster without having to modify any portion of the original software. The key to our transparent migration lies in hiding the complexity associated with the multi-node execution (such as coordinating the processes running in the cluster nodes) inside the generic-aligner framework. Moreover, following the design and operation in our Message Passing Interface (MPI) version of HPG Aligner RNA BWT, we organize the framework into two stages in order to be able to execute different aligners in each one of them. With this configuration, for example, the first stage can ideally apply a fast aligner to accelerate the process, while the second one can be tuned to act as a refinement stage that further improves the global alignment process with little cost.

Improving the network scalability of Erlang

As the number of cores grows in commodity architectures so does the likelihood of failures. A distributed actor model potentially facilitates the development of reliable and scalable software on these architectures. Key components include lightweight processes which ‘share nothing’ and hence can fail independently. Erlang is not only increasingly widely used, but the underlying actor model has been a beacon for programming language design, influencing for example Scala, Clojure and Cloud Haskell.While the Erlang distributed actor model is inherently scalable, we demonstrate that it is limited by some pragmatic factors. We address two network scalability issues here: globally registered process names must be updated on every node (virtual machine) in the system, and any Erlang nodes that communicate maintain an active connection. That is, there is a fully connected O(n2) network of n nodes.We present the design, implementation, and initial evaluation of a conservative extension of Erlang — Scalable Distributed (SD) Erlang. SD Erlang partitions the global namespace and connection network using s_groups. An s_group is a set of nodes with its own process namespace and with a fully connected network within the s_group, but only individual connections outside it. As a node may belong to more than one s_group it is possible to construct arbitrary connection topologies like trees or rings.We present an operational semantics for the s_group functions, and outline the validation of conformance between the implementation and the semantics using the QuickCheck automatic testing tool. Our preliminary evaluation in comparison with distributed Erlang shows that SD Erlang dramatically improves network scalability even if the number of global operations is tiny (0.01%). Moreover, even in the absence of global operations the reduced connection maintenance overheads mean that SD Erlang scales better beyond 80 nodes (1920 cores).

Fuzzy Multi-criteria Approach for Component Selection of Fault Tolerant Software System under Consensus Recovery Block Scheme

The vibrant and advanced software development tools not only provide software with versatile functions for radical users but at the same time, an easy to use GUI for naive users. APS (Application Package Software) has provided a customised approach for developing independent software components which are ready to be integrated with existing software systems. The APS along with CBSE (Component Based Software Engineering) has an inordinate potential for reducing development time, cost and effort, which otherwise may extend beyond weeks or months’ time for integration. Further, the CBSE approach promotes software reusability i.e. reusing the available components. A component can be reused after fabrication which will include the fabrication cost and time. For development of economical and reliable software, components can be procured in the form of Commercial off-The Shelf (COTS) components from the vendor or may be developed in-house or can be fabricated. This decision is based on several parameters. The aim of this paper is to select the suitable mix of components using Build-or-buy strategy or considering fabrication and to propose a multi-objective model for software modular system with objective of maximizing reliability while simultaneously minimizing the cost, execution time and Source Lines of Code (SLOC) using Consensus Recovery Block Scheme.

센서네트워크 어플리케이션을 위한 네트워크 프레임워크와 통합시뮬레이터 간의 인터페이스 구현 및 설계

For the development of reliable software, Software testing is the most important. Recently small changes of the software according to the importance of regression testing is growing. To verify Application of a large number of nodes, Network simulator environment is required. This paper proposed interface module between network framework for sensor network application and co-simulator to unit test sensor network application. To conclude, developer can focus on sensor network application implementation only, so the improved integrated simulator contributes to increase development productivity.

Return on Investment and Effort Expenditure in the Software Development Environment

Software development is tedious, expensive and requires lot of resources and investment. Justification of the resources and investments are highly required in the software industry. Development of reliable and quality software has become increasingly important in today’s world. A static model of the software development life cycle that treats all modules similarly is becoming inadequate to the task. In this paper efforts are made to quantify the concept of return on investment and factors responsible for improving the return on investment. In the second part of the paper, we are trying to justify the effort expenditure and how to optimize the effort expenditure.

Developing Reliable yet Flexible Software through If-Then Model Transformation Rules

Developing reliable yet flexible software is a hard problem. Although modeling methods enjoy a lot of advantages, the exclusive use of just one of them, in many cases, may not guarantee the development of reliable and flexible software. Formal modeling methods ensure reliability because they use a rigorous approach to software development. However, lack of knowledge and high cost practically force developers to use semi-formal methods instead. Semi-formal (visual) modeling methods, which are widely used in practical large-scale software development, are not good enough for reliable software development. This paper proposes a new approach to the development of reliable yet flexible software by transforming formal and semi-formal models into each other. In this way, the advantages of both methods are incorporated in to the software development process. The structured rules, proposed in this paper, transform formal and visual models into each other through the iterative and evolutionary process. The feasibility as well as the effectiveness of the proposed approach is demonstrated using the multi-lift system as a test case.

How to measure and optimize reliable embedded software

This tutorial explores some of the challenges of measuring performance and timing behaviour of reliable embedded systems and explains techniques and strategies for optimization of reliable software. The tutorial explains and compares different techniques for measurement and analysis of software on embedded targets including tracing methods, in-memory analysis and using hardware support. It shows how those techniques can be used for verification of non-functional properties on-target, including in the context of DO178B/C and the new ISO26262 standard to meet the requirements for safety in automobiles. The tutorial presents optimization at a high and low level, looking at strategies and the tradeoffs that occur in reliable software development, introducing a process that helps to ensure that optimization can have the maximum benefit for the minimum effort.