An analysis of tools for automatic software development and automatic code generation

Wind energy has the advantages of wide distribution, renewable, and non-polluting, so it is receiving more and more attention from more and more countries. As more and more wind power systems are integrated into the grid, it has an impact on the stability of the grid. To keep the power system stable, there is an urgent need for a grid simulator that can simulate various behaviors of the grid and test the reliability of the wind turbine before grid integration. Inverters, especially multilevel inverters, as the core part of the grid simulator, have been widely studied by scholars in recent years. However, compared to conventional inverters, multilevel inverters are characterized by high code development effort, great difficulty, and a long development period. In this paper, we adopt an automatic DSP code generation method with MATLAB hardware support package and give a complete system design method and development flow based on MATLAB and TMSF28335 automatic code generation. Finally, we take the closed-loop three-level MMC inverter as an example, propose an equalization algorithm suitable for automatic code generation for the capacitor-voltage balancing part, and verify the feasibility of the DSP automatic code generation in a multilevel inverter development. The feasibility of DSP automatic code generation in the development of a multilevel inverter is verified. The experimental results show that the proposed equalization algorithm with variable reference coefficient and DSP automatic code generation method can be used in the development of a multilevel inverter, which can improve development efficiency and reduce development costs.

The low-code approach is an important area of research being developed to improve the rapid creation and performance of software applications. This approach allows developers and users to easily create software applications through an interactive interface. Existing research shows that the low-code approach accelerates the development process in innovative ways, saves effort and time, and reduces code complexity. This study analyses and compares automatic code generation and transformation techniques in low-code platforms. The study analyses the contributions of automatic code generation and transformation to software engineering processes and evaluates the impact of these techniques on software product quality and development speed. The importance of the Model-Based Development (MBD) approach in automated code generation processes is highlighted. It is stated that MBD provides benefits such as speeding up the software development process and reducing errors by enabling automatic code generation from high-level abstract models. In the study, various literature studies were evaluated to examine the impact of automatic code generation and transformation techniques on the application development process. These studies have shown how automatic code generation and transformation techniques are applied, what results are achieved and what contributions these techniques make to the software development process. The results of the study show that automatic code generation and transformation techniques have a great impact on the application development process. In particular, the acceleration of the software development process, the reduction of errors and the ability to produce more effective and efficient software products are important benefits of these techniques. In addition, it has been found that this approach increases the success of software projects by enabling software developers to produce fast and accurate solutions in complex systems. In this context, the importance of automatic code generation and transformation techniques plays an important role in software development processes.

Nanosatellite on-board software is a real-time system that schedules and executes control actions over the platform and the payload subsystems during the mission stages. Its development is a complex task that can be better approached using advanced software engineering techniques as graphical component based modelling and automatic code generation.Nanosat1B is a scientific nanosatellite developed by the Spanish National Institute of Aerospace Technology (INTA) that was launched on July 09. This paper introduces the component base modelling and automatic code generation of Nanosat1B on-board software using a CASE tool named EDROOM. It shows the UML2 diagrams used for specifying the system components, their interfaces and behaviour, emphasizing on their reuse possibilities on the same domain.The paper describes also the main characteristics of the EDROOM tool and analyses the feasibility of its adaptation for automatic Java code generation. The benefits of using the EDROOM automatic Java code generation for nanosatellites on-board software development are also enumerated.

In this paper incorporating manual and automatic code generation is discussed. A solution for automatic metadata-driven code generation is presented illustrated with multi tier Enterprise Resource Planning System. We intend to make our solution available to public in order to encourage investigation of code generation and schema-driven tools for .NET Framework.

Generating source code from a model, using a model-driven development techniques (MDD), rapidly increases application reliability and substantially accelerates time-to-market, thereby reducing application development costs. The generated source code does not have its functional implementation, so besides MDD, it is necessary to create some additional tools that will fulfill this gap. This paper describes a design of a system for automatic model development and automatic source code generation that transforms specification into the product implementation. Specification contains data necessary for the implementation of GUI controls. Controls are integrated as a part of an Eclipse based IDE. To reduce the amount of source code that needs to be generated, reflection API Java feature is used. Reflection API is also used for developing JUnit Java tests, that verify the reliability of generated source code. Using the proposed algorithm 1960 GUI controls and 3920 JUnit tests are generated. Based on this approach it is estimated that time-to-market is reduced four and a half months and quality of source code is increased significantly, lowering the total cost of developing and maintaining application.

Automatic code generation is a standard method in software engineering since it improves the code consistency and reduces the overall development time. In this context, this paper presents a design flow for automatic VHDL code generation of mppSoC (massively parallel processing System-on-Chip) configuration. Indeed, depending on the application requirements, a framework of Netbeans Platform Software Tool named MppSoCGEN was developed in order to accelerate the design process of complex mppSoC. Starting from an architecture parameters design, VHDL code will be automatically generated using parsing method. Configuration rules are proposed to have a correct and valid VHDL syntax configuration. Finally, an automatic generation of Processor Elements and network topologies models of mppSoC architecture will be done for Stratix II device family. Our framework improves its flexibility on Netbeans 5.5 version and centrino duo Core 2GHz with 22 Kbytes and 3 seconds average runtime. Experimental results for reduction algorithm validate our MppSoCGEN design flow and demonstrate the efficiency of generated architectures.

In order to improve the efficiency and quality of software development, automatic code generation technology is the current focus. The quality of the code generated by the automatic code generation technology is also an important issue. However, existing metrics for code automatic generation ignore that the programming process is a continuous dynamic changeable process. So the metric is a dynamic process. This article proposes a metric method based on dynamic abstract syntax tree (DAST). More specifically, the method first builds a DAST through the interaction in behavior information between the automatic code generation tool and programmer. Then the measurement contents are extracted on the DAST. Finally, the metric is completed with contents extracted. The experiment results show that the method can effectively realize the metrics of automatic code generation. Compared with the MAST method, the method in this article can improve the convergence speed by 80% when training the model, and can shorten the time-consuming by an average of 46% when doing the metric prediction.

Software engineering has been established with various development methodologies and software development process models, which have enabled increases in software production efficiency and improvements on product quality. However, the time taken to develop software and input human resources are the main causes of cost increases. Thus, the project will be delayed as the time taken to complete iterative tasks increases. In this regard, this paper designs an automatic source code generation system using meta data based on user patter definition in order to resolve the problem.

Extension of the Modeling Tool Suite for Development of Embedded Systems for the Space Domain

The increasing reliance on automatic code generation integrated with Generative AI technology has raised new challenges for cybersecurity defense against code injection, insecure code templates, and adversarial manipulation of an AI model. These risks make developing advanced frameworks imperative to ensure secure, reliable, and privacy-preserving code generation processes. The paper presents a novel Hybrid Artificial Neural Network (ANN)-Interpretive Structural Modeling (ISM) Framework to alleviate the cybersecurity risks associated with the automatic code generation using Generative AI. The proposed framework integrates the predictive capability of ANN and structured analysis of ISM for the identification, evaluation, and treatment of common vulnerabilities and risks in automatic code generation. We first conduct a multivocal literature review (MLR) to identify cybersecurity risks and generative AI practices for addressing these risks in automatic code generation. Then we conduct a questionnaire survey to identify and validate the identified risks and practices. An expert panel review was then assigned for the process of ANN-ISM. The ANN model can predict potential security risks by learning from historical data and code generation patterns. ISM is used to (1) structure and visualize (2) relations between identified risks and mitigation approaches and (3) offer a combined, multi-layered risk management methodology. We then perform an in-depth examination of the framework with a case study of an AI-based code generation company. We further determine its practicality and usefulness in real-world settings. The case study results show that the framework efficiently handles the primary cybersecurity challenges, such as injection attacks, code quality, backdoors, and lack of input validation. The analysis characterizes the maturity of several mitigation practices and areas for improvement for security integration with automatic code generation functionality. Advanced risk mitigation is enabled in the framework across multiple process areas, where techniques such as static code analysis, automated penetration testing, and adversarial training hold much promise. The Hybrid ANN-ISM Mechanism is a stable and flexible solution for cybersecurity risk reduction in automatic code generation environments. The coupling of ANN and ISM, in terms of predictive analysis and structured risk management, respectively, contributes effectively towards the security of AI-based code generation tools. More research is required to improve the scalability, privacy preserving, and dynamic integration of the framework with cybersecurity threat intelligence.

Embedded real-time systems employ a variety of operating system platforms. Consequently, for automatic code generation, considerable redevelopment is needed when the platform changes. This results in major challenges with respect to the automatic code generation process of the architecture analysis and design language (AADL). In this paper, we propose a method of template-based automatic code generation to address this issue. Templates are used as carriers of automatic code generation rules from AADL to the object platform. These templates can be easily modified for different platforms. Automatic code generation for different platforms can be accomplished by formulating the corresponding generation rules and transformation templates. We design a set of code generation templates from AADL to the object platform and develop an automatic code generation tool. Finally, we take a typical Data Processing Unit (DPU) system as a case study to test the tool. It is demonstrated that the auto-generated codes can be compiled and executed successfully on the object platform.

Formal methods are very efficient techniques for formal verification of a specification and to find errors in early stage of the system development. In order to generate a high quality code from a formal specification particularly in the embedded system is highly indispensable and a de-facto standard in many industrial application domains, such as medical, avionics and automotive control. This paper presents automatic source code generation from the developed formal specifications of a cardiac pacemaker. Cardiac pacing system is a Grand Challenge in the area of Software Verification. This paper includes an architecture of automatic code generation tool, summary of a formal development of the cardiac pacemaker using refinement techniques in Event-B, code generation of the developed formal model into C, C++, Java and C\# using code generation tool EB2ALL, and finally the code compilation and execution.

Automatic code generation based on Coloured Petri Net (CPN) models is challenging because CPNs allow for the construction of abstract models that intermix control flow and data processing, making translation into conventional programming constructs difficult.We introduce Process-Partitioned CPNs (PP-CPNs) which is a subclass of CPNs equipped with an explicit separation of process control flow, message passing, and access to shared and local data. We show how PP-CPNs caters for a four phase structure-based automatic code generation process directed by the control flow of processes. The viability of our approach is demonstrated by applying it to automatically generate an Erlang implementation of the Dynamic MANET On-demand (DYMO) routing protocol specified by the Internet Engineering Task Force (IETF).

<div class="htmlview paragraph">Model-based software development and automatic code generation have become increasingly established in recent years. The automotive industry has widely adopted and successfully deployed these methods in many different series production programs worldwide. This brought various benefits, such as a reduction in development times, improved quality due to more precise specifications, and early verification and validation by means of simulation.</div> <div class="htmlview paragraph">At the same time, more and more safety-related and safety-critical systems have been - and will be -introduced into modern vehicles. Common examples are active front steering, adaptive cruise-control, and integrated chassis control.</div> <div class="htmlview paragraph">This leads to the question, if and how model-based design and automatic production code generation can be applied to the development of safety-critical systems.</div>

This article presents a comprehensive analysis of the transformative impact of Generative Artificial Intelligence (GenAI) on software development practices. Through systematic evaluation of implementations across multiple development environments, this article demonstrates how GenAI technologies significantly enhance four critical areas of the software development lifecycle: code generation, automated testing, intelligent code review, and predictive failure analysis. The findings indicate a 45% reduction in initial code development time through automated code generation, 60% improvement in test coverage through AI-driven test case creation, and 30% decrease in post-deployment issues through predictive failure analysis. The article employs a mixed-methods approach, combining quantitative analysis of development metrics from 50 software projects with qualitative assessments from 200 professional developers across diverse organizational contexts. Results reveal that organizations implementing GenAI-assisted development workflows experience a mean productivity increase of 37% while maintaining or improving code quality metrics. Additionally, the article identifies key integration challenges and provides a framework for effective GenAI adoption in software development environments. These findings contribute to the growing body of knowledge on AI-assisted software development and offer practical insights for organizations seeking to optimize their development processes through GenAI integration.