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 applic...
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ISBN:
(纸本)9798400709463
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 softwareengineering 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 softwaredevelopment 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 softwaredevelopment 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 softwaredevelopment 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 import
To effectively regulate software update practices in the vehicle industry and ensure the security and reliability of update activities, China officially issued the mandatory national standard GB 44496-2024 "Gener...
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software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). this study investigates the effectiveness of using source code for fault predic...
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software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). this study investigates the effectiveness of using source code for fault prediction in SOS. It uses supervised machine learning algorithms such as random forest, decision tree, and support vector machine to improve error prediction accuracy. Feature extraction is used for more accurate analysis. the study highlights the strengths and weaknesses of these algorithms, providing insights into the prediction of malicious software in SOS. It aims to provide high-performance and reliable software architecture, and advance fault prediction models in SFP.
In order to create softwarethat is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cos...
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In order to create softwarethat is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cost-effective and successful advanced bug prediction model presents challenges. this research endeavor aims to achieve precise bug identification by exploring the utilization of various machine learning techniques on training and testing datasets. Multiple machine learning methods have been devised to identify and learn from software defects. this study employs machine learning techniques to conduct a comprehensive examination of software bug detection, offering valuable insights to the software industry. It synthesizes existing research on bug prediction, detailing different methods and highlighting their effectiveness, advantages, and limitations. this comprehensive analysis offers valuable guidance to researchers and software developers seeking to enhance bug detection methods for the creation of higher-quality software.
this research concludes an overall summary of the publications so far on the applied Machine Learning (ML) techniques in different phases of softwaredevelopment Life Cycle (SDLC) that includes Requirement Analysis, D...
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ISBN:
(纸本)9789897586477
this research concludes an overall summary of the publications so far on the applied Machine Learning (ML) techniques in different phases of softwaredevelopment Life Cycle (SDLC) that includes Requirement Analysis, Design, Implementation, Testing, and Maintenance. We have performed a systematic review of the research studies published from 2015-2023 and revealed that software Requirements Analysis phase has the least number of papers published;in contrast, software Testing is the phase withthe greatest number of papers published.
the softwaredevelopment life cycle (SDLC) is incomplete without the software testing phase. It's the act of checking that a piece of software really does what it's supposed to. Test case creation is one of th...
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the softwaredevelopment life cycle (SDLC) is incomplete without the software testing phase. It's the act of checking that a piece of software really does what it's supposed to. Test case creation is one of the testing tasks that have a major impact on the quality and speed with which the process is completed. Research into the automated production of test cases has been extensive because of the time and energy it can save over the human method of creating test cases. While most of the recommended methods are based on UML models, other publications have given a specifications-based method of creating test cases. this literature analysis focuses on automated test case generation strategies based on use case specifications and the techniques used to verify them. the analysis also highlights the ways in which the methods diverge when used to solving certain pressing problems in software testing.
Code recommendation plays a crucial role in programming, assisting programmers in improving code quality, efficiency, and maintainability, thereby better addressing complex softwaredevelopment tasks. In recent years,...
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ISBN:
(纸本)9798350386783;9798350386776
Code recommendation plays a crucial role in programming, assisting programmers in improving code quality, efficiency, and maintainability, thereby better addressing complex softwaredevelopment tasks. In recent years, artificial intelligence has been widely applied in softwareengineering domains such as code recommendation, particularly in the field of static languages, where significant advancements have been made in intelligent code recommendation. However, for dynamic languages like Python, intelligent code recommendation faces numerous challenges, such as the difficulties in dynamic analysis and code data imbalance. In light of this, building upon previous work, this paper achieves more accurate Python code recommendation by representing code context with semantically richer graphs and employing conditional generative adversarial networks for data augmentation of code semantic graphs, mitigating the impact of data imbalance. Experimental results demonstrate that the proposed code recommendation method, GraphPyRec, can effectively recommend Python code, and the data augmentation method, GraphGAN, can significantly optimize the recommendation model.
through visual research on the current situation and development trends of China's industrial software industry explore its research hotspots and future directions. A total of 1812 articles were searched on China ...
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Dependability is an intrinsic attribute of a system and a key decision factor for evaluating and accepting successful system performance. the dependability of a system is affected by different factors in different sta...
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ISBN:
(纸本)9798350386783;9798350386776
Dependability is an intrinsic attribute of a system and a key decision factor for evaluating and accepting successful system performance. the dependability of a system is affected by different factors in different stages of its life cycle, and it must be properly managed. the key to achieve dependability is to adopt a life-cycle approach to obtain dependability. this paper analyzes the meaning of dependability life cycle, and on the basis of introducing the existing system, data and product life cycle models, constructs the system dependability life cycle model, including concept, development, realization, utilization and retirement/re-use of five life cycle stages. Considering the wide application of software system, the dependability life cycle model of software system is further studied and established. the implementation of dependability activities in each phase of the system life cycle can promote the realization of system dependability and create dependability value.
the softwaredevelopment life cycle (SDLC) is a structured process for delivering a high-quality product within a specified timeframe. Developing a cost-effective and efficient software product life cycle has always b...
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