Analysis and Comparison of Automatic Code Generation and Transformation Techniques on Low-Code Platforms

The development of avionics software demands more and more human intervention. The software development process is rather highly expensive and risky since it must undergo certification. Automation of such a process can reduce errors and human interaction. Automated development tools usually deal with automatic code generation. These approaches are actually based on pre-defined fixed rules and policies to automatically generate software code which still demands considerable hand-coded software. This paper presents a self-directed development process that is able to make decisions by its own to generate software code. The automated decision-making process relies on a reasoner, and an ontological database that captures development skills from human developers as well as the dynamic configurations and capabilities of the system under development. The knowledge captured enables highlevel reasoning to automatically generate the control software code. A case study based on the development of Embedded Control Software (ECS) for Distributed Aircraft Fuel Management (DAFM) is presented. Details of the reasoning ontology to develop ECS, and experimental results from ECS for DAFM automatically generated are presented.

Incorporation of modeling and autocode technologies into the systems and software development process opens new ways for increasing quality of products and faster delivery to the customer. We describe the practical steps of the Matlab/Simulink/Stateflow model-based development process with the focus on automatic code generation as part of the process. We discuss a way to flawless integration of autocode technology into the established software production development process. We describe the structure of an autocode-ready Simulink/Stateflow model, shows the effect of code generator usage at the stage of software verification and introduces a formal method to estimate the development time for autocoded software. We list the tools and utilities that Delphi has developed to support the transition to model-based and autocode-based software development process.

Supply Chain Monitoring (SCMo) systems are able to capture, monitor and provide correct real-time state information about physical activities in supply chain processes for Event Management, which improves efficiency and effectiveness of Supply Chain Management (SCM). Unfortunately, due to various logistic application scenarios, SCMo systems are complex and expensive to build. This paper proposes a software design and development process of MDSD, with which a SCMo system can be generated semi-automatically and quickly.

Several difficulties commonly arise during the software development process. Among them are the lengthy technical process of developing a system, the limited number and technical capabilities of human resources, the possibility of bugs and errors during the testing and implementation phase, dynamic and frequently changing user requirements, and the need for a system that supports multi-platforms. Rapid application development (RAD) is the software development life cycle (SDLC) that emphasizes the production of a prototype in a short amount of time (30-90 days). This study discovered that implementing a model-driven architecture (MDA) approach into the RAD method can accelerate the model design and prototyping stages. The goal is to accelerate the SDLC process. It took roughly five weeks to construct the system by applying all of the RAD stages. This time frame does not include iteration and the cutover procedure. During the prototype test, there were no errors with the create, read, update, and delete (CRUD) procedure. It was demonstrated that automatic transformation in MDA can shorten the RAD phases for designing the model and developing an early prototype, reduce code errors in standard processes like CRUD, and construct a system that supports multi-platform.

In particular, an early integration of performance specifications in the SDP has been recognized during the last few years as an effective approach to improve the overall quality of a software. Performance related problems are becoming more and more strategic in the software development, especially recently with the advent of Web Services and related business-oriented composition techniques (software as a service, Web 2.0, orchestration, choreography, etc.). The goal of our work is the definition of a software development process that integrates performance evaluation and prediction. The software performance engineering development process (SPEDP) we specify is focused on performance, which plays a key role driving the software development process, thus implementing a performance/QoS-driven (software) development process. More specifically, in this paper our aim is to formally define the SPEDP design process, posing particular interest on the basis, on the first step of SPEDP, the software/system architecture design, modeling and/or representation. We define both the diagrams to use and show how to model the structure of the software architecture, its behavior and performance requirements. This is the first mandatory step for the automation of the SPEDP into a specific tool.

Performance related problems are becoming more and more strategic in the software development, especially recently with the advent of Web Services and related business-oriented composition techniques (software as a service, Web 2.0, orchestration, choreography, etc.). In particular, an early integration of performance specifications in the SDP has been recognized during the last few years as an effective approach to improve the overall quality of a software. The goal of our work is the definition of a software development process that integrates performance evaluation and prediction. The software performance engineering development process (SPEDP) we specify is focused on performance, which plays a key role driving the software development process, thus implementing a performance/QoS-driven (software) development process. More specifically, in this paper our aim is to formally define the SPEDP design process, posing particular interest on the basis, on the first step of SPEDP, the software/system architecture design, modeling and/or representation. We define both the diagrams to use and show how to model the structure of the software architecture, its behavior and performance requirements. This is the first mandatory step for the automation of the SPEDP into a specific tool, which we have partially implemented as a performance plug-in for ArgoUML, ArgoPerformance.

This scientific article presents the results of a study focused on the current practices and future prospects of AI-tools usage, specifically large language models (LLMs), in software development (SD) processes within European IT companies. The Pan-European study covers 35 SD teams from all regions of Europe and consists of three sections: the first section explores the current adoption of AI-tools in software production, the second section addresses common challenges in LLMs implementation, and the third section provides a forecast of the tech future in AI-tools development for SD. The study reveals that AI-tools, particularly LLMs, have gained popularity and approbation in European IT companies for tasks related to software design and construction, coding, and software documentation. However, their usage for business and system analysis remains limited. Nevertheless, challenges such as resource constraints and organizational resistance are evident. The article also highlights the potential of AI-tools in the software development process, such as automating routine operations, speeding up work processes, and enhancing software product excellence. Moreover, the research examines the transformation of IT paradigms driven by AI-tools, leading to changes in the skill sets of software developers. Although the impact of LLMs on the software development industry is perceived as modest, experts anticipate significant changes in the next 10 years, including AI-tools integration into advanced IDEs, software project management systems, and product management tools. Ethical concerns about data ownership, information security and legal aspects of AI-tools usage are also discussed, with experts emphasizing the need for legal formalization and regulation in the AI domain. Overall, the study highlights the growing importance and potential of AI-tools in software development, as well as the need for careful consideration of challenges and ethical implications to fully leverage their benefits.

Model-Based Development (MBD) provides an additional level of abstraction, the model, which lets engineers focus on the business aspect of the developed system. MBD permits automatic treatments of these models with dedicated tools like synthesis of system's application by automatic code generation. Real-Time and Embedded Systems (RTES) are often constrained by their environment and/or the resources they own in terms of memory, energy consumption with respect to performance requirements. Hence, an important problem to deal with in RTES development is linked to the optimization of their software part. Although automatic code generation and the use of optimizing compilers bring some answers to application optimization issue, we will show in this paper that optimization results may be enhanced by adding a new level of optimizations in the modeling process. Our arguments are illustrated with examples of the Unified Modeling Language (UML) state machines diagrams which are widely used for control aspect modeling of RTES. The well-known Gnu Compiler Collection (GCC) is used for this study. The paper concludes on a proposal of two step optimization approach that allows reusing as they are, existing compiler optimizations.

In software development, the user interface(UI) design process involves the part from an imagination that begins on a piece of paper where designers share thoughts to the final prototype that is ready to implement. UI designers use a technique of paper prototyping in low-fidelity sketches to implement the conceptual design. Once a design is drawn, it is usually captured within a photograph, then converted to a high-level mockup, and manually translated into a working client-side code by developers. The process consumes considerable effort and also delays the design process. Recently, researchers attempted to recreate UI from their sketches using code generation techniques. In this paper, we present a system for automatic code generation that combines recent advances in computer vision and deep learning for translating a sketched wireframe into a UI skeleton code. The proposed system has two main components: component recognition and code generation. The component recognition learns to extract visual features and their spatial layouts from sketched UI images. The code generator creates the equivalent UI skeleton codes based on the information from the detection model. A layout algorithm has been developed to resolve the overlapping of components and structure the layout. The evaluation result of the component recognition model indicates that the Faster R-CNN with Inception ResNet v2 backbone model achieved a mean average precision (mAP) of 95.5 %, among the other trained models. To evaluate the generator, we conducted a user study based on visual similarities between input image and the rendered UI.

The Model-Driven Development paradigm aims to represent all the information system features through models. Conceptual-Model Programming offers a similar approach, but with a focus on automatic code generation. Both approaches consider modeling and traceability of different abstraction levels, where each level can be tackled with different modeling methods. This heterogeneity introduces a challenge for the quality of the traceability and transformations among models, especially when aiming for automatic code generation. In this paper, we introduce a holistic conceptual-model programming method to generate code from different abstraction levels (from the problem space to the solution space), through three modeling languages whose consistency has been ontologically ensured by two transformation techniques. Particularly, we focus on transformations from the strategic layer using i*, to business process layer using Communication Analysis (CA), and to the system conceptual model layer with OO-Method, which can automatically generate fully functional systems. Even though there are previous works that have proposed partial transformations among these modeling methods, this paper is the first one that deals with the perspective of putting together all the models in a single development method. For each transformation, we discuss what parts can be automatically performed and what parts need human intervention.

Automatic code generation for production automotive powertrain applications introduces many challenges for the tool vendor. Resource efficiency, reliability, and product process development issues need to be addressed. Automatic code generation used for rapid prototyping systems usually will not satisfy the requirements necessary for production oriented code generation. Some of the requirements critical to automatic code generation are not immediately obvious to a tool vendor who is not active in the user's industry. The purpose of this paper is to address some of the most common requirements for production automotive powertrain code generation.

. This paper presents a multi-language and multi-environment generation of nonlinear finite element codes. The system consists of two major components. The Mathematica package AceGen is used for the symbolic derivation of formulae needed in numerical procedures and automatic code generation. The Computational Templates package is a collection of prearranged modules for the automatic creation of the interface between the finite element code and the finite element environment. The AceGen and the Computational Templates packages together enable multi-language and multi-environment generation of nonlinear finite element codes from the same symbolic description. The approach implemented in AceGen avoids the usual problem of uncontrollable growth of expressions by combining several techniques: symbolic and algebraic capabilities of Mathematica, an automatic differentiation technique, automatic code generation, simultaneous optimization of expressions; and theorem proving by stochastic evaluation of the expressions.

Many papers proposed in the software engineering and information systems literature are dedicated to analysis of software projects missing their schedules, exceeding their budgets, delivering software products with poor quality and in some cases even wrong functionality. The expression “software crisis“ has been coined since the late 60's to illustrate this phenomenon. Various solutions has been proposed by academics and practitioners in order to deal with the software crisis, counter these trends and improve productivity and software quality. Such solutions recommend software process improvement as the best way to build software products needed by modern organizations. Among the well-known solutions, many are based either on software development tools or on software development approaches, methods, processes, and notations. Nevertheless, the scope of these solutions seems to be limited and the improvements they provide are often not significant. We think that since software artifacts are accumulation of knowledge owned by organizational stakeholders, the software crisis is due to a knowledge gap resulting from the discrepancy between the knowledge integrated in software systems and the knowledge owned by organizational actors. In particular, integrating knowledge management in software development process permits reducing the knowledge gap through building software products which reflect at least partly the organization's know-how. In this paper, we propose a framework which provides a definition of knowledge based on information systems architecture and describes a knowledge-oriented software development process to help organizations in reducing the software crisis impacts.

The digital signal processing (DSP) processor-in-the-loop tests based on automatic code generation technology are studied. Firstly, the idea of model-based design is introduced, and the principle and method of embedded code automatic generation technology are analyzed by taking the automatic code generation of the DSP control algorithm for pulse width modulation (PWM) output as an example. Then, the control system model is established on MATLAB/Simulink. After verifying the model through simulation, the target board platform is established with DSP as the core processor, and the automatically generated code is tested by the processor-in-the-loop (PIL). The results show that the technology greatly shortens the development cycle of the project, improves the robustness and consistency of the control code, and can be widely used in the complex algorithm development process of the controller, from intelligent design and modeling to implementation.

Human resource management practices are key for the success of software development projects. Practices that promote knowledge sharing and organizational learning are positively related to development---effort curves, and thus software companies are looking for different alternatives oriented to promoting these practices. The model-driven development (MDD) paradigm is positioned as one of the best alternatives for reutilization of development knowledge. In particular, this paradigm considers the specification of conceptual models that can be used as input for automatic code generation to different target platforms. However, testing of applications developed through MDD solutions is still performed by the manual definition and execution of test cases by testers, which negatively impacts in the time reduction obtained from automatic code generation and the reutilization of knowledge generated during the MDD project execution. To address this issue, this paper presents a testing approach that automatically generates executable test cases for software developed by using MDD technologies.