Software Units Research Articles

Agile Methodologies In The Automotive Industry: A Comprehensive Survey

The automotive industry is undergoing a major transformation driven by trends such as autonomous driving, connectivity, electrification, and rapid digitalization. This results in embracing agile software development practices and new digital architectures to accelerate development cycles and manage increasing complexity. This paper discusses the insights on the agile methodologies into automotive software procedures, extending agility beyond software units , integrating agile with product lines, addressing security concerns, and constructing advanced digital platforms for automotive systems also assessment models like ASPLA (Agile Software Product Line Adoption) for agile adoption in software product lines, and future research directions towards realizing the vision of intelligent, self-driving, connected vehicles through continuous evolutions in agile-based development processes, digital architectures complying with quality standards, cross-domain collaborations, and empirical validations

Code-Aware Prompting: A Study of Coverage-Guided Test Generation in Regression Setting using LLM

Testing plays a pivotal role in ensuring software quality, yet conventional Search Based Software Testing (SBST) methods often struggle with complex software units, achieving suboptimal test coverage. Recent work using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance. As a result LLM-generated testsuites still suffer from low coverage. In this paper, we present SymPrompt, a code-aware prompting strategy for LLMs in test generation. SymPrompt’s approach is based on recent work that demonstrates LLMs can solve more complex logical problems when prompted to reason about the problem in a multi-step fashion. We apply this methodology to test generation by deconstructing the testsuite generation process into a multi-stage sequence, each of which is driven by a specific prompt aligned with the execution paths of the method under test, and exposing relevant type and dependency focal context to the model. Our approach enables pretrained LLMs to generate more complete test cases without any additional training. We implement SymPrompt using the TreeSitter parsing framework and evaluate on a benchmark challenging methods from open source Python projects. SymPrompt enhances correct test generations by a factor of 5 and bolsters relative coverage by 26% for CodeGen2. Notably, when applied to GPT-4, SymPrompt improves coverage by over 2x compared to baseline prompting strategies.

A statistical model for estimating size and productivity ratio in SDLC

During a software development life cycle, one has to estimate the effort and schedule required to produce a software unit. Estimates for The effort and schedule are derived from other measurements such as size and productivity Ratio. Size may be estimated using methods such as Function Point, WBS, LOC, etc., and the Productivity Ratio is expressed as Size / Time to complete the Unit of Software. Such estimates are static and do not consider real-time Parameters which cause variations from the estimate. In this paper, a statistical model for Size and Productivity Ratios is derived using Historical values after considering several factors influencing the size and the productivity ratio of produced software. Typically, an estimate of the effort and schedule is required to complete. A software work product an enhancement request or a software fix. We use Function Points or WBS to estimate the size of the software in consideration. We use baseline values for estimating productivity Ratios. Real-time values however differ from Baseline values as they are influenced by several factors. Here are some factors which would influence the Actual Size of the software Later on we will use Statistical techniques of Multivariable Parameter estimation and logistic Regression to derive run time equations of Size and Productivity Ratio.

Predicting the Number of Software Faults using Deep Learning

The software testing phase requires considerable time, effort, and cost, particularly when there are many faults. Thus, developers focus on the evolution of Software Fault Prediction (SFP) to predict faulty units in advance, therefore, improving software quality significantly. Forecasting the number of faults in software units can efficiently direct software testing efforts. Previous studies have employed several machine learning models to determine whether a software unit is faulty. In this study, a new, simple deep neural network approach that can adapt to the type of input data was designed, utilizing Convolutional Neural Networks (CNNs) and Multi-Layer Perceptron (MLP), to predict the number of software faults. Twelve open-source software project datasets from the PROMISE repository were used for testing and validation. As data imbalance can negatively impact prediction accuracy, the new version of synthetic minority over-sampling technique (SMOTEND) was used to resolve data imbalance. In experimental results, a lower error rate was obtained for MLP, compared to CNN, reaching 0.195, indicating the accuracy of this prediction model. The proposed approach proved to be effective when compared with two of the best machine learning models in the field of prediction. The code will be available on GitHub.

Implementation Genetic Algorithm for Optimization of Kotlin Software Unit Test Case Generator

Unit testing has a significant role in software development and its impacts depend on the quality of test cases and test data used. To reduce time and effort, unit test generator systems can help automatically generate test cases and test data. However, there is currently no unit test generator for Kotlin programming language even though this language is popularly used for android application developments. In this study, we propose and develop a test generator system that utilizes genetic algorithm (GA) and ANTLR4 parser. GA is used to obtain the most optimal test cases and data for a given Kotlin code. ANTLR4 parser is used to optimize the mutation process in GA so that the mutation process is not totally random. Our model results showed that the average value of code coverage in generated unit tests against instruction coverage is 95.64%, with branch coverage of 76.19% and line coverage of 96.87%. In addition, only two out of eight generated classes produced duplicate test cases with a maximum of one duplication in each class. Therefore, it can be concluded that our optimization with GA on the unit test generator is able to produce unit tests with high code coverage and low duplication.

Software implementation of the algorithm for automatic detection of lineaments and their properties on open-pit dumps

The paper presents an algorithm and a description of its software implementation for detecting lineaments (ground erosions or cracks) in aerial photography images of open-pits. The proposed approach is based on the apparatus of convolutional neural networks based on the semantic classification of binarized images of objects (lineaments), as well as graph theory for determining the geometric location of linearized objects, followed by determining their lengths and areas. Three-channel RGB images of high-resolution aerial photography (pixel 10x10 cm) were used as initial data. The software unit of the model is logically divided into three layers: pre-processing, detection and post-processing. The first level includes preprocessing of input data to form a training sample based on successive transformations of an RGB image into a binary one using the OpenCV library. A neural network of the U-Net type, which includes blocks of the convolutional (Encoder) and scanning parts (Decoder), represents the second level of the information model. At this level, automatic lineament detection (washouts) is implemented. The third level of the model is responsible for calculating the areas and lengths of lineaments. The result of the work of the convolutional neural network is transferred to the input. Lineament area is calculated by summing the total number of points multiplied by the pixel size. The length of the lineaments is computed by linearizing a plane object into a line segmental object with nodal points and then calculating the lengths between them, also relying on the resolution of the original image. The software module can work with input images, with their subsequent resulting merging to the size of the original image.

Simulation of Integration of Distributed Generation into Power System Control

The paper deals with modeling of theintegration of distributed generation into the central dispatching system with regard to the electricity market environment. Due to the large number of the relative small unit power of distributed generation the resulting reserve power can achieve a remarkable quantity. However the utilization of these reserves is not obvious because of several technical issues like the cogeneration of heat power, lack of proper informatics, bounds of law about electric energy (LEE) in Hungary etc. An agent based simulation model was developed to discuss the problem. The model consists of the distributed generation units (DG-s) and the DG-concentrator (DGC) which is an interface between DG-s and the independent transmission system operator (TSO). All of them are simulated as autonomous software units suitable for decision making, learning and multi-direction communication. This is a multi-agent system, where the service, control and simulation of stochastic demand variation are easy to study. Several scenarios were discussed regarding the contracts between DG-s and TSO. The financial paragraph of the contract should ensure the willingness of private owned DG-s to provide their reserves for the central control. The results of the simulation help to find the minimum loss operation of the system.

A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm

The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.

Model-Based Testing Approaches using UML Diagrams: A Systematic Literature Review

Software Unit Testing (SUT) is the starting point for Model-Based Testing (MBT), a testing method. The Unified Modeling Language (UML) has become the standard for modelling software in professional and academic settings. There are various uses for the modelling language known as UML. The findings of an SLR on UML-based model-based testing methodologies are presented in this paper. Thirty-five primary articles about six research issues were examined using selection and exclusion criteria. Methods, model class, intermediate format use, and testing methodology are the primary points of examination. The review outcomes identify future research needs and avenues of inquiry.

Relationship between Bioelement Concentrations in the Human Body and Psycho-Functional Variables Observed in Neurotic Disorders

Introduction: Trace elements are involved in brain functioning, ensuring basic neurochemical processes in the central nervous system and neuropsychological functions. Trace element deficiency is a challenge in northern regions, where acclimatization-related lack of many essential elements is aggravated by adverse health effects of a combination geochemical and anthropogenic factors. Objective: To establish relationships between the elemental profile of the body and some psychophysiological characteristics of young residents of the continental part of the Magadan Region as a model presumably healthy part of the population. Materials and methods: Fifty-five lyceum students, including 25 girls and 30 boys (mean age = 17.6 ± 0.25 years), living in the town of Susuman, Magadan Region, were examined. Concentrations of 25 trace elements (Al, As, B, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, I, K, Li, Mg, Mn, Na, Ni, P, Pb, Se, Si, Sn, V, and Zn) were measured in hair samples of the subjects using inductively coupled plasma atomic emission spectrometry (ICP-AES). Psychophysiological indicators were examined using the NS-Psychotest hard & software unit. The I.N. Gurvich questionnaire was applied to study neuropsychic adaptation (NPA) and anxiety levels were determined using the Spielberger-Hanin test. Results: The correlation analysis showed the highest number of links between lithium and psycho-functional variables in all students, which is consistent with the opinion of experts on the role of this element in reducing neurological manifestations, stress, and depression. The female group demonstrated relationships between the indicator of situational anxiety and those of the functional state of the central nervous system: functionality level (r = –0.56), reaction stability (r = –0.63), and functional ability level (r = –0.61), p < 0.01. Cluster analysis was represented by four clusters, the largest of which included calcium and indicators of velocity of neural responses, the second – indicators of alexithymia, potassium, sodium, magnesium and phosphorus, the third grouped zinc and velocity of a simple visual motor response, and the fourth combined indicators of a simple visual motor response and choice reaction. Conclusion: The survey revealed a higher risk of neurotic disorders, cognitive impairment, and chronic stress in the adolescents against the background of the region-attributed deficiency in such trace elements as manganese, iodine, cobalt, and selenium and high levels of iron and silicon. Endemic hypothyroidism and diabetes mellitus are likely to develop with age in northerners due to high frequency of thyroid-specific bioelement deficiencies and disorders of chromium and vanadium metabolism.

Dynamic Canonical Data Model: An Architecture Proposal for the External and Data Loose Coupling for the Integration of Software Units

Integrating third-party and legacy systems has become a critical necessity for companies, driven by the need to exchange information with various entities such as banks, suppliers, customers, and partners. Ensuring data integrity, keeping integrations up-to-date, reducing transaction risks, and preventing data loss are all vital aspects of this complex task. Achieving success in this endeavor, which involves both technological and business challenges, necessitates the implementation of a well-suited architecture. This article introduces an architecture known as the Dynamic Canonical Data Model through Agnostic Messages. The proposal addresses the integration of loosely coupled software units, mainly when dealing with internal and external data integration. To illustrate the architecture’s components, a case study from the Mexican Logistics Company Paquetexpress is presented. This organization manages integrations across several platforms, including SalesForce and Oracle ERP, with clients like Amazon, Mercado Libre, Grainger, and Afull. Each of these incurs costs ranging from USD 30,000 to USD 36,000, with consultants from firms such as Quanam, K&F, TSOL, and TekSi playing a crucial role in their execution. This consumes much time, making maintenance costs considerably high when clients request data transmission or type changes, particularly when utilizing tools like Oracle Integration Cloud (OIC) or Oracle Service Bus (OSB). The article provides insights into the architecture’s design and implementation in a real-world scenario within the delivery company. The proposed architecture significantly reduces integration and maintenance times and costs while maximizing scalability and encouraging the reuse of components. The source code for this implementation has been registered in the National Registry of Copyrights in Mexico.

The CEBPA-FGF21 regulatory network may participate in the T2DM-induced skeletal muscle atrophy by regulating the autophagy-lysosomal pathway.

Recent years have witnessed an increasing research interest in the roles of transcription factor (TF)-gene regulatory network in type 2 diabetes mellitus (T2DM). Thus, we sought to characterize the mechanistic insights based on the TF-gene regulatory network in skeletal muscle atrophy in T2DM. Differentially expressed TFs (DETFs) and mRNAs (DEmRNAs) were obtained in T2DM-related gene expression profiles (GSE12643, GSE55650, GSE166502, and GSE29221), followed by WGCNA, and GO and KEGG enrichment analyses. Next, the iRegulon plug-in unit of Cytoscape software was used to construct a TF-mRNA regulatory network. Besides, RT-qPCR and ChIP-seq were utilized to measure the expression of CEBPA and FGF21 in the skeletal muscle tissues or cells of T2DM rat models. At last, the effect of overexpression of FGF21 on the autophagy-lysosomal pathway was examined in skeletal muscle cells of T2DM rats. Totally, 12 DETFs and 102 DEmRNAs were found in the skeletal muscle tissues of T2DM samples. The DEmRNAs were mainly enriched in the autophagy-lysosomal pathway. CEBPA affected the skeletal muscle atrophy in T2DM by regulating 5 target genes via the autophagy-lysosomal pathway. CEBPA could target FGF21. In addition, the expression of CEBPA was elevated, while the expression of FGF21 was diminished in the skeletal muscle tissues or cells of T2DM rats. The CEBPA-FGF21 regulatory network promoted skeletal muscle atrophy in T2DM by activating the autophagy-lysosomal pathway. The CEBPA-FGF21 regulatory network may participate in the T2DM-induced skeletal muscle atrophy by regulating the autophagy-lysosomal pathway. Thus, our study provides interesting targets for prevention of skeletal muscle atrophy in T2DM.

Devising a method for determining the aperture brightness of an object using a typical shape of its image

The object of this study is the aperture brightness of the image of the object, which has a variety of typical shapes in the frames of the series. It directly depends on the stability of the shooting conditions of the objects under study. Thus, determining the exact aperture brightness of an object in the frame becomes more difficult. For this purpose, a method was devised for determining the aperture brightness of an object using the typical shape of its image on a series of frames. This method is based on the formation of a typical shape of a digital image of an object based on data from all frames of the series. The typical shape makes it possible to take into account the peculiarities of the formation of the image of the object on each frame of the series. Based on this, a more accurate estimate of the initial approximation of the parameters of all Gaussian images of the object is performed. In addition, the adaptation of the method to the standard shape makes it possible to perform a more accurate assessment of the aperture brightness of the object in comparison with the analytically defined profile. An estimate of the aperture brightness of the object was derived using the least squares method. Due to minimization using the Levenberg-Marquardt algorithm, the use of the method improved identification with reference objects and reduced the number of false positives. The study showed a decrease in the standard deviation of frame identification errors by 5–7 times when using a typical shape of a digital image. The devised method for determining an object's aperture brightness using its image's typical shape was tested in practice within the framework of the CoLiTec project. It was implemented in the intra-frame processing unit of the CoLiTecVS software for the automated construction of brilliance curves of the studied variable stars. Owing to the use of the CoLiTecVS software and the proposed computational method implemented in it, more than 700,000 measurements of various objects under study were successfully processed and identified

소프트웨어 교육과 연계한 인공지능 교육 프로그램 개발 및 적용

Artificial intelligence (AI) software technology is a key innovation that will change human society and lead future industries. Accordingly, the government is developing various educational policies to revitalize artificial intelligence education. However, AI education is not organized as a subject or unit in the current elementary school curriculum, making it difficult to learn. The present study developed an AI image recognition technology education program for elementary school students in connection with a software unit of a practical course subject and aimed to confirm the effectiveness of the program to create an artificial intelligence class that can utilize the curriculum time.
 For the present analysis, 50 students in two classes of the 6th grade of an elementary school were divided into an experimental group and a control group. The experimental group was subjected to the AI image recognition technology education program linked with the software education in the 20th session, while the control group underwent the software class using the textbook in the 20th session to analyze the improvement of computing thinking ability, STEAM Literacy and software education satisfaction. Results of the study revealed that the experimental group showed statistically significant differences in computing thinking. Furthermore, the experimental group showed statistically significant differences in the convergence competency of STEAM Literacy compared to the control group. Moreover, the experimental group’s software education satisfaction test result was higher than that of the control group. The hereby interpreted results indicate that it is possible to apply AI education to elementary school curriculum through this program. Furthermore, it might have a positive effect on students' learning competency, as the hereby performed experiment indicates.

From Inheritance to Mockito: An Automatic Refactoring Approach

Unit testing focuses on verifying the functions of individual units of a software system. It is challenging due to the high inter dependencies among software units. Developers address this by mocking—replacing the dependency by a “fake” object. Despite the existence of powerful, dedicated mocking frameworks, developers often turn to a “hand-rolled” approach—inheritance. That is, they create a subclass of the dependent class and mock its behavior through method overriding. However, this requires tedious implementation and compromises the design quality of unit tests. This work contributes a fully automated refactoring framework to identify and replace the usage of inheritance by using Mockito—a well received mocking framework. Our approach is built upon the empirical experience from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">five</i> open source projects that use inheritance for mocking. We evaluate our approach on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nine</i> other projects. Results show that our framework is efficient, generally applicable to new datasets, mostly preserves test case behaviors in detecting defects (in the form of mutants), and decouples test code from production code. The qualitative evaluation by experienced developers suggests that the auto-refactoring solutions generated by our framework improve the quality of the unit test cases in various aspects, such as making test conditions more explicit, as well as improved cohesion, readability, understandability, and maintainability with test cases. Finally, we submit 23 pull requests containing our refactoring solutions to the open source projects. It turns our that, 9 requests are accepted/merged, 6 requests are rejected, the remaining requests are pending (5 requests), with unexpected exceptions (2 requests), or undecided (1 request). In particular, among the 21 open source developers that are involved in the reviewing process, 81% give positive votes. This indicates that our refactoring solutions are quite well received by the open source projects and developers.

NEURAL NETWORK SUPPORT FOR AUTOMATION OF OBJECT CONTROL WITH MULTIFACTORIAL INPUT UNDER CONDITIONS OF UNCERTAINTY

High dimension of the factors, their noisiness, subjectivity of the human factor breed high uncertainty in the implementation of effective monitoring and productive management in the areas of production and consumption, and hinder the optimal decisions making. In these conditions, it is appropriate to apply intelligent data analysis procedures based on artificial neural networks. Purpose of the study is to substantiate the technology for constructing of effective neural network models for automatic assessment of the object states and their control by finding the optimal values of input factors based on the analysis of the initial set of retrospective data. The desired product is set of neural network models for simultaneous assessment of the current object states and the calculation of the input factors values that ensure the achievement of the required objective function indicators. To automate the processes of recognizing the states of the study object and adapting the factors that bring the current state to the target one, a functional dependence of the states and factors is found based on forced learning of a synthesized models ensemble. The proposed technology and technical tools make it possible to automate the processes of classifying the states of the study objects, adapt the input factors to the target states, and evaluate the quality by model testing. The practical significance of the study results is in the creation of a universal toolkit for a whole class of objects in the tasks of automatic state classification and search for input factors space that is adequate to the target state space. Functionally, the ensemble of trained models can be implemented as a data analysis software unit in the format of two subsystems: for recognizing the states of the study object and adapting input factors to target states. Automation of the state classifying tasks and adapting the input set in the proposed technology that is performed on the basis of standard technical data analysis packages, makes it possible to increase the efficiency of decision-making and reduce financial costs in the implementation of industrial and commercial projects.

Virtualization Airborne Trusted General Computing Technology

Aircraft information service systems, such as airborne information systems, airborne integrated maintenance management systems, and cabin management systems, have greatly improved the ease of use and maintenance of civil aircraft. The current computing platforms used for accommodating these systems are unable to satisfy the multifaceted requirements of future information-based aircraft, such as energy conservation, emission reduction, high-performance computing, and information security protection, due to their high computing capacity, weight, and power consumption. Based on multi-core multi-threaded processors, a security hardware unit with microkernel virtualization technology and a virtualization airborne trusted general computing service architecture is proposed, and key technologies, including a high-performance processing and high-security hardware unit, virtualization management software unit, and virtualization security protection architecture were designed. By building a verification environment, the proposed platform was verified in terms of its application accommodation function, platform performance, and network security protection, for comparison with the existing platforms. The results showed that our method can fulfill the requirements of these technical indicators and is applicable, not only to new-generation civil aircraft, but also to unmanned aerial vehicles (UAVs) and emergency rescue aircraft with high-performance safety-critical computing needs.

Assisted discovery of optimal solution for logistics problems using ontology modelling

The logistics processes conjointly execute various physical and logical tasks. The physical tasks may concern the elements such as goods to be transported, vehicles, and human resources. Whereas, the logical tasks are generally accomplished by software units that can be fully automatic or interactive such as optimisation methods or event management, etc. The logistics problems are often complex np-hard combinatorial optimisation problems. In this paper, we define the shared conceptual vocabulary concerning the logistics problems and inherent optimisation solutions. The result of the work is a knowledge base system, which is formalised and implemented as a set of ontologies. These have been used as key components of a tool that may assist the logistics expert to identify the related optimisation methods to solve the concerned problem and eventually list the available corresponding web-services implementing them.

Fast Augmented Reality Authoring: Fast Creation of AR Step-by-Step Procedures for Maintenance Operations

Augmented Reality (AR) has shown great potential for improving human performance in Maintenance, Repair, and Overhaul (MRO) operations. Whilst most studies are currently being carried out at an academic level, the research is still in its infancy due to limitations in three main aspects: limited hardware capabilities, the robustness of object recognition, and content-related issues. This article focuses on the last point, by proposing a new geometry-based method for creating a step-by-step AR procedure for maintenance activities. The Fast Augmented Reality Authoring (FARA) method assumes that AR can recognise and track all the objects in a maintenance environment when CAD models are available, to knowledge transfer to a non-expert maintainer. The novelty here lies in the fact that FARA is a human-centric method for authoring animation-based procedures with minimal programming skills and the manual effort required. FARA has been demonstrated, as a software unit, in an AR system composed of commercially available solutions and tested with over 30 participants. The results show an average time saving of 34.7% (min 24.7%; max 55.3%) and an error reduction of 68.6% when compared to the utilisation of traditional hard-copy manuals. Comparisons are also drawn from performances of similar AR applications to illustrate the benefits of procedures created utilising FARA.

초등 실과 교과서의 소프트웨어 단원에 대한 발문 유형 분석

This study analyzed practical elementary school textbooks to ascertain the types of questions asked on software (SW) programming units, with the aim of identifying a writing direction for an impending curriculum revision. In this context, the scope of analysis was limited to units corresponding to “programming structures and elements.” To classify relevant questions, we used a framework from a previous study that analyzed types of questions pertaining to “SW and robot unit.” The results were as follows.&#x0D; First, the ratio of closed questions (71.03%) was approximately 2.45 times higher than the ratio of open questions (28.97%). Second, the characteristics of the type of question by publisher were the same in that all six publishers had a higher closed questions than the open questions ratio, but there were publishers who did not use memory questions(A, D) and divergent thinking questions(C) depending on how they were composed. Third, our analysis of the types of questions found in each unit component showed that most recognitional questions were provided in the introduction and developing sections, while most evaluation questions were provided in the organizing section.&#x0D; To summarize, we found a higher ratio of closed questions (which restrict student thinking) than open questions These results are expected to be used as basic data for writing SW programming units for practical arts textbooks.