REST API Architecture Design on Multi-Platform Device Development

The rapid development of artificial intelligence technology has provided brand-new ideas and methods for computer application software development, becoming an important driving force to promote the intelligence and efficiency of software development. Starting from the overall concepts of artificial intelligence technology and computer application software development, this paper systematically analyzes the application of artificial intelligence in computer software development, and elaborates on the functions of artificial immune technology, neural network technology, Agent technology, and expert systems in software development. It not only provides theoretical support for the application of artificial intelligence technology in software development but also offers operable strategic suggestions for practice, which is of great guiding significance for improving software development efficiency and optimizing software quality.

Perkembangan smartphone pada saat ini semakin pesat yang disebabkan oleh adanya berbagai aplikasi mobile yang ditawarkan dalam mempermudah aktivitas manusia. Salah satu aplikasi mobile yang banyak dibahas akhir-akhir ini adalah MyPertamina. MyPertamina adalah sebuah aplikasi mobile yang digunakan untuk membeli berbagai produk yang ditawarkan oleh Pertamina secara online yang dapat dilakukan kapan pun dengan berbagai jangkauan area pembelian. Dalam pengembangnya terbagi menjadi beberapa modul yaitu payment, voucher, user, dan loyalty. Salah satu modul yang cukup penting adalah modul loyalty yang berisi fitur-fitur tentang keuntungan pengguna. Pengembangan aplikasi MyPertamina hampir keseluruhan menggunakan RESTful API. Dalam proses pengembangan perangkat lunak dengan menggunakan RESTful API, perlu dilakukan pengujian terhadap RESTful API yang telah dibuat untuk meminimalisir kesalahan yang ada pada RESTful API. Terdapat berbagai metode pengujian perangkat lunak salah satunya adalah pengujian Black Box. Pengujian Black Box berfokus pada keluaran yang dihasilkan oleh sistem dan tidak melakukan pengujian pada kode sumber. Pada pengujian Black Box terdapat beberapa teknik pengujian salah satunya adalah Equivalence Partitioning. Penelitian melakukan perbandingan pengujian manual terhadap RESTful API dan mengimplementasikan pengujian otomatis menggunakan pytest. Pengujian manual dilakukan menggunakan postman. Sedangkan pengujian otomatis dijalankan menggunakan pytest. Kedua pengujian yang telah dilakukan memberikan hasil bahwa RESTful API yang dikembangkan telah sesuai dan berjalan dengan baik. Kata kunci: Equivalance Partitioning, MyPertamina, Pengujian Perangkat Lunak, RESTful API

Testing RESTful APIs thoroughly is critical due to their key role in software integration. Existing tools for the automated generation of test cases in this domain have shown great promise, but their applicability is limited as they mostly rely on random inputs, i.e., fuzzing. In this paper, we present RESTest, an open source black-box testing framework for RESTful web APIs. Based on the API specification, RESTest supports the generation of test cases using different testing techniques such as fuzzing and constraint-based testing, among others. RESTest is developed as a framework and can be easily extended with new test case generators and test writers for different programming languages. We evaluate the tool in two scenarios: offline and online testing. In the former, we show how RESTest can efficiently generate realistic test cases (test inputs and test oracles) that uncover bugs in real-world APIs. In the latter, we show RESTest's capabilities as a continuous testing and monitoring framework. Demo video: https://youtu.be/1f_tjdkaCKo.

With the increasing percentage penetration of wind energy, accurate Wind Speed/Power Forecasting (WSF/WPF) has become a crucial tool for power trading and electricity grid operations. The physical and statistical methods used to forecast mostly rely on meteorological information, site specific data and historical data from wind farms. Most of the existing works are limited to utilize single input data source and a prediction variable. However, some gap in using multiple data sources and multiple prediction variables as input has been identified to substantiate and enhance forecast accuracy of these models. In this paper, a multi-source and multivariate surveillance approach has been conducted using time series methods and machine learning algorithms. Firstly, the proposed model uses multiple sources to understand the deciding prediction variables. Secondly, an analogy among time series Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Networks (RNNs) with Long Short-term Memory units (LSTMs) is used. Although, the prediction models using single data source are economical for forecast providers and wind power producers considering multiple sources can certainly improve the forecasting accuracy thus reducing wind curtailments and imbalance penalties. A comparative approach has been made using data sources obtained for Gulf of Khambhat, a south to north penetration of the Arabian Sea on the western shelf of India. The obtained results show that the forecasting accuracy can be improved by considering multiple sources of input data.

Single source refers to the unified measurement of different aspects of the same individual based on data from multiple sources. In the context of UGC, single source data can be used to study at least two important but as yet insufficiently investigated theoretical issues. First, single source data are ideal sources for studying inter-platform dynamics such as user migration across UGC platforms. Second, single source data can help to link individual self-reported cognitive factors with web crawled individual behavior logs, to achieve better understanding of individual behavior. In this paper, we select a random sample of Sina Blog users and collect their behavior information on both Sina Blog and Sina Weibo platforms; we also conduct an online survey to collect information about their cognitive factors. Merging all data together, we observe and quantify different behavior patterns of the same people across Blog and Weibo; we also identify alternative attractiveness and perceived popularity as significant drivers of one of the most important inter-platform dynamics---switching behavior.

In recent years, with the rapid development of information technology, the application of computer technology has become increasingly widespread. In order to meet the needs of modern society for computer technology, computer software needs to be constantly developed. In the development of computer software, layered technology occupies a very important position. It is essential for the development of computer software. It can achieve multi-level technology across. Therefore, the various layering techniques in computer software development are analysed, in order to explore the application of layered technology in computer software development.

Humans are complex and their behaviors follow complex multi-modal patterns, however to solve many social computing problems one often looks at complexity in large-scale yet single point data sources or methodologies. While single data/single method techniques, fueled by large scale data, enjoyed some success, it is not without fault. Often with one type of data and method, all the other aspects of human behavior are overlooked, discarded, or, worse, misrepresented. We identify this as two succinct problems. First, social computing problems that cannot be solved using a single data source and need intelligence from multiple modals and, second, social behavior that cannot be fully understood using only one form of methodology. Throughout this talk, we discuss these problems and their implications, illustrate examples, and propose new directives to properly approach in the social computing research in today's age.

This work proposes a generalized excel based test automation framework for REST API testing using groovy script. Testing is an important process in software development process, which helps to find unknown errors, failure and bugs in a software application and increases quality of the software application. With the existing process of testing is manual, where the inputs are given manually and the actual outcome or output is validated for different test case scenarios. This manual testing leads to increase in time and effort of testing, as there will be different scenarios to be tested in which different test data has to be prepared to test a particular functionality. As the functionalities in an application changes or added up, over the software development phase, there is a need to test the old functionalities after and before, integrating the new or changed functionality to the existing system. This results in testing the already tested functionalities to test again manually, which increases effort and time for the testing process. The manual testing process has to be automated, so that the testing time, effort and money can be reduced. The test automation framework is implemented using a groovy compiling tool in which the functionalities such as, read the data from input test data excel file, make a REST API request, validate the data and writing the result to the excel file has been developed. Since groovy is a flavor of java, several libraries have been imported to implement the test automation framework.

Satellite aerosol products provide valuable large-scale atmospheric information for environment and climate research. Nevertheless, limitations due to cloud contamination and retrieval assumptions often result in significant gaps in Aerosol Optical Depth (AOD) observations, diminishing their representativeness and utility. Therefore, we propose an adaptive spatiotemporal reconstruction method (Spatio-Temporal Reconstruction with Ecw and Auds Method, STREAM) based solely on a single data source, which integrates Empirical Correlation Weighting (ECW) for interpolation with Adaptive Up/Down Scaling (AUDS) for seamless reconstruction. This method was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) AOD over the North China Plain (NCP) from 2002 to 2023 at a 0.05° resolution. Case comparisons demonstrate that STREAM efficiently fills data gaps, and the STREAM AOD presents strong concordance with both the DB AOD and reference datasets. Cross-validation indicates that as the missing rate rises, the correlation (R) between the STREAM AOD and the DB AOD decreases, while Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) increase. Validation against AERONET data shows that STREAM AOD achieves an R value of 0.88, RMSE of 0.29, and MAE of 0.18 for STREAM AOD, with 52.39% of the data falling within the expected error range. Compared to Long-term Gap-free High-resolution Air Pollutants (LGHAP) AOD, our approach reveals minor discrepancies in values and spatial distribution despite relying on a single data source. The robust performance of STREAM AOD in the NCP highlights potential applicability to utilize in broader regions as well as other atmospheric remote sensing products.

Software development has been fundamentally re-imagined by cloud technology through scalable, available-on-demand services and resources. Enter Infrastructure as a Service (IaaS), followed by Platform as a Service (PaaS) and now Software as a Service (SaaS); these models have disrupted the traditional development landscape enabling organizations to concentrate on their primary applications without investing heavily in infrastructure. And integration of DevOps and Continuous Integration/Continuous Deployment (CI/CD) practices helped to unify the cycle even more by speeding up updates and automating deployments. Cloud technology may offer many benefits, but also brings with it some challenges including data security and privacy both at stake as well as vendor lock-in translates in the form of integration cost impacting legacy infrastructure. In this paper, we will discuss the changes that have occurred in software development with cloud technology and how it works.

With the increasing range of software development and applications, the scale of software development is becoming larger and larger. The larger software's scale is the more possible failed in the phase of design, development, and implementation in software. As a starting point to improve the management and development of software, this paper designs a service platform for data services - JCFXBL. JCFXBL is mainly used to solve two issues, one is the management of software development, and the other is the technology of software development. In combination with two the practical application cases based on JCFXBL, from software development, software design, project management, the article describes the role of JCFXBL platform in the “Project Management” and “systems integration projects”, and verifies that JCFXBL platform can reduce the complexity of software development, improve software quality and solve software development, management issues, by actual software development.

The quest by E&P companies and their service providers to gain a competitive edge through technology is driving a demand for higher standards in engineering software. Time to market is a critical success factor and is often viewed as contradictory to higher standards and other factors such as limited resources and budgets. An optimum approach to engineering software development can successfully satisfy both time-to-market and budgetary requirements while continually raising standards. This paper will discuss software practices that have proven effective during a full oilfield engineering software application development life cycle. Although the process activities – including specification, design, development, testing and deployment – were conventional, they were sequenced in a non-conventional manner and augmented with techniques such as risk management, use cases analysis, software modeling and configuration management. The paper presents a case study that illustrates how these practices were used in a sand control engineering software development project to successfully design and remodel an existing horizontal gravel pack spreadsheet application into a standalone desktop application. The new application provides a user-friendly interface to design and analyze gravel transport for solids of varying density in Newtonian fluids. A computation engine was developed as a plug-in component to perform various computations during the gravel packing process. An object-oriented architecture and framework were designed and built to avoid having to rebuild every new application from scratch. The authors will explain how the system was built and extended to incorporate new capabilities and how adopting optimized development practices has enabled reuse of software components in new applications and increased both the standards and cost-effectiveness of engineering software development.

This paper mainly analyzes the specific application of the computer software data mining technology of association analysis, fully excavates the important data information in the computer software according to the principle of association analysis, and further puts forward the development rules of association analysis to improve the computer software database. With the continuous development of computer software technology, the application of computer software will produce large-scale application data, file resources and so on. Data mining technology abstracts the characteristic information needed in data analysis according to the data analysis object determined by computer software, and then selects the appropriate information collection method to store the collected information in the computer information database. It is precisely because of the in-depth application and development of computer software by data mining technology that the data processing speed and storage space of computer software have been steadily improved, so as to promote the good operation of association analysis rules of computer software.

The evolution of Internet has produced profound impacts on software development technologies, operation forms, and service models. In particular, the underlying crowd intelligence mechanisms in large-scale crowd collaboration practices, such as open sourcing and crowdsourcing, have greatly inspired the software development. Thus, this study investigates the innovation and production models for such open-sourcing and crowdsourcing practices. Based on this analysis, we propose the crowd-intelligence-based software development mechanisms including crowd collaboration, open sharing, and continuous evaluation. Specifically, we study the basic environment, the mechanism model, and the key technologies of crowd collaboration. Based on these aspects, we comprehensively discuss the crowd-intelligence-based software development environment, the involved core factors, and our open-sourcing practice. Finally, we propose the great challenges to be addressed in the future, and hope these studies and discussions are helpful.

We understand benchmarking as the comparison of research software performance under controlled conditions. It encompasses both the technical performance, including software quality metrics, and the scientific performance in predefined challenges. Scientific communities play an important role here as they are responsible for defining reference datasets and metrics, pointing out the existing scientific challenges in their respective fields. Thus, in the context of ELIXIR-EXCELERATE project, we have developed the OpenEBench platform (https://openebench.bsc.es) aiming to provide a reference place to host technical and scientific performance for research software across the life sciences. OpenEBench provides an infrastructure where end-users can learn from different available software options and select the one best fitting their scientific needs. Bioinformatics software developers can find relevant datasets and meaningful scientific challenges to evaluate their own developments, and communities interested in a particular scientific domain can easily define which datasets and metrics are relevant for developers to work on, which in turn will allow the field to move ahead. A web application (https://openebench.bsc.es/html/scientific) allows users to browse through the benchmarking results from the different communities engaged (TCGA, QFO, CAMEO, GMI) in the platform which can be viewed using one of the visualization charts and transformed to table format, which is easier to interpret by non-expert users. On the technical side, the application uses several REST APIs, which can be used by other developers to upload and access the data for future studies or use cases. Another key feature is the Virtual Research Environment (https://openebench.bsc.es/submission), which includes the necessary mechanisms to import and execute benchmarking workflows on top of cloud computing infrastructures. OpenEBench follows the recommendations made by ELIXIR on the development of open source software making its code publicly available at https://github.com/inab/openebench-hub.