Java B eans and Software Architecture

The object-oriented theory has been widely used in software design and development and Java programming language is a popular object-oriented programming language for computer science related majors in higher education. With the huge demand for intelligent manufacturing employees in the work market, more and more universities have listed Java programming language in their curriculum for non-CS undergraduates. However, learning object-oriented programming languages, such as Java, is tough even for CS students. This paper presents how to improve non-CS undergraduate students’ learning outcomes of java language by adopting industry standard professional integrated development tool Eclipse and interactive visual educational tool BlueJ. Our Java programming language course is an alternative 8-week course including 16 h lecture and 16 h lab. Forty-seven junior students from mechanical engineering major were divided into two groups, 23 in the control group (CG) and 24 in the experimental group (EG). Students in both groups used Eclipse to write and run programs during the course while BlueJ was adopted to demonstrate the object-oriented concepts during the fifth and sixth weeks only for students in the EG. A pre-test, which was taken by students from both groups, shows no statistical differences in programming knowledge between the two groups. Results from the post-test at the end of the sixth week show students in EG achieved better performance than students in CG. Results of the survey for the EG reveal that students thought BlueJ was helpful for mastering the concepts of object-oriented programming.KeywordsJava programmingLearning outputHigher educationInteractiveVisualBlueJEclipse

The emergence of Unified Modeling Language (UML) as a standard for modeling systems has encouraged the use of automated software tools that facilitate the development process from analysis through coding. The UML state machine diagram is used for modeling the dynamic aspects of systems and includes many concepts that are not present in most popular programming languages, like Java or C++. There exists a gap between high level modeling language and a programming language. This paper proposes an approach to narrow the gap between multiple UML models and an implemented system. The narrowing of gap is achieved by generating compact and efficient Java code directly from UML state machine having sequential and concurrent substates. The code generation is achieved by creating a mapping between UML behavior and the Java programming language. Our model can work as a basis for automatic code generation for object-oriented systems.

Java programming language is one of the most popular programming languages among IT developers. There are common problems in the beginning learning process such as the installation and configuration process of java software like JDK (Java Development Kit). During the online class session, it takes a lot of time and a large internet data packet to solve this problem. This research proposes a website-based system called livE (online - java Exercise). Students can practice coding and also do exams in real-time, simply by using a web browser and the internet. If lecturers want to make corrections, lecturers do not need to download student answer files. They can directly run the program code from student answers in the livE system. This indicates that the system implementation provides process efficiency in the student's job sheet exercises and lecturer's corrections. The result from the test that has been carried out shows that livE is running as expected. Usability testing shows that users are "totally agree" with system quality.

Python has emerged as one of the most popular programming languages, extensively utilized in domains such as machine learning, data analysis, and web applications. Python’s dynamic nature and extensive usage make it an attractive candidate for dynamic program analysis. However, unlike for other popular languages, there currently is no comprehensive benchmark suite of executable Python projects, which hinders the development of dynamic analyses. This work addresses this gap by presenting DyPyBench, the first benchmark of Python projects that is large-scale, diverse, ready-to-run (i.e., with fully configured and prepared test suites), and ready-to-analyze (by integrating with the DynaPyt dynamic analysis framework). The benchmark encompasses 50 popular open-source projects from various application domains, with a total of 681k lines of Python code, and 30k test cases. DyPyBench enables various applications in testing and dynamic analysis, of which we explore three in this work: (i) Gathering dynamic call graphs and empirically comparing them to statically computed call graphs, which exposes and quantifies limitations of existing call graph construction techniques for Python. (ii) Using DyPyBench to build a training data set for LExecutor, a neural model that learns to predict values that otherwise would be missing at runtime. (iii) Using dynamically gathered execution traces to mine API usage specifications, which establishes a baseline for future work on specification mining for Python. We envision DyPyBench to provide a basis for other dynamic analyses and for studying the runtime behavior of Python code.

With the continuous development of China’s economy and society, computers have become an indispensable part of our lives. The development of intelligent life and the normal operation of computers cannot be separated from software development. The Java programming language has become the key to computer software development and is gradually becoming well-known. Based on its unique advantages, JAVA programming language has broad development prospects, and we must conduct comprehensive research on it. This article first highlights the specific features of VF programming language and C++language. This article will mainly start with an overview of the Java programming language, explore all its characteristics, and then analyze the types of Java programming languages required in computer software development.

Industry 4.0 energy monitoring system for multiple production machines

The object of research in the article are the tools and capabilities of the C# programming language for the implementation of cybersecurity analysis software in local computer networks and computer-integrated systems. The relevance of the study is due to the importance of information security of computer and computer-integrated systems in government, military-industrial complex, private business etc., and due to the importance of training cybersecurity professionals in higher education to consider teaching examples in popular programming languages. The goal of the work is to research the possibilities of the C# programming language for the development of software that analyzes cybersecurity in local computer networks and computer-integrated systems. The tasks to be solved are: to develop software for scanning network device ports in computer networks and computer-integrated systems for information security audit, using tools and libraries of the C# programming language, to research the benefits and possibilities of using this programming language for this task. Research methods: theory of computer networks, object-oriented programming, theory of algorithms and data structures, theory of software testing. Conclusions. In this paper the possibilities and advantages of the C# programming language for developing cybersecurity analysis software for computer and computer-integrated systems were explored. In the course of work software that analyzes information security in local computer networks and computer-integrated systems was developed. This software can be used for educational purposes in learning the C# programming language and cybersecurity of computer systems. The developed software has the potential to be further improved and applied in various fields to test the cybersecurity of local computer networks and computer-integrated technologies.

With the maturity of network technology, the deterministic factor of a 'successful' web site does not as much depend on its connectivity to the Internet, rather the content of web pages plays a more important role. The inclusion of dynamic data, for example, has become an increasingly desirable feature of a web page. Web servers that support dynamic web pages are capable of retrieving data at the time of user requests. Rather than being statically included in the web page, these dynamically retrieved data may physically reside in various file systems or database servers across the Internet.JAVA, a new object-oriented programming language, has become a buzzword since its introduction by Sun Microsystems in 1994 [1, 2, 3]. It was claimed that JAVA, via its ability to embed applets in a web page, would make the content of a web page alive and dynamic. Data and information on a web page can now be updated dynamically when users request them.In this paper, we discuss our experience of developing JAVA applets that use various types of JDBC (JAVA Database Connectivity) drivers to connect to and then manipulate data stored in SQL (Structured Query Language) database servers. We first start with the discussion of general client/server model involving JDBC, and then introducing the procedure of setting up programming projects employing this approach in an undergraduate database course.

The latest rankings of computer language popularity once again list C as the most popular programming language [1]. As a computer scientist that has written a lot of C code over the years, I must admit that makes me smile. While I don't write code much anymore, I like writing code, and I like writing it in C. Apparently so do a lot of other people. However, C was also the most popular programming language 25 years ago which is one indication that, in the field of software development, not much has changed. Are software developers unwilling to accept new paradigms or are the new paradigms proposed to-date simply unacceptable? This talk discusses what has and hasn't changed in the area of software development, how change (or the lack of it) relates to application security, and concludes with some thoughts on possible directions for the future.

Rapidly identifying and mitigating security threats to reduce the impact of attacks is one of the most pressing challenges of our time. Digital threats frequently jeopardize users, often manifested as web-based, intranetwork, or spam-related attacks. The literature indicates that most studies examine each type of attack separately. This study introduces six distinct deep learning (DL) model architectures capable of detecting three attacks. The results of this study demonstrate that the Deep Neural Network (DNN) model achieved a test accuracy of 98.78% for the FWAF dataset. The CNN-LSTM model achieved 99.95% accuracy for the KDDCUP-99 dataset and 99.09% accuracy for the SMS Spam dataset. This study accurately identifies anomalous activities and anticipates potential attacks by analyzing large data sets from various data channels. Furthermore, this work improves the efficacy of existing firewalls and holds significant promise in preventing malicious factors from exploiting vulnerabilities in digital infrastructures. This study conducted experiments in a real-time environment using artificial intelligence models deployed on Jetson Nano. For malicious traffic detection, malicious traffic was created on the network with the Nmap tool, and network traffic was monitored using Wireshark, followed by classification using our model. In the Damn Vulnerable Web Application (DVWA) platform, a malicious URL classification model was used to test web attacks. For spam detection, suspicious text was retrieved from the user via a plugin running in the browser and sent to the spam classification model, and the result was returned to the user. In conclusion, DL models have the potential to detect and prevent various security threats in web applications. This study enables the analysis of normal operating behaviors in web applications and the identification of anomalous activities. This capability helps detect attempted intrusions or potential security issues indicating user behavior. Furthermore, the study facilitates the automatic detection of attacks targeting web applications and the implementation of countermeasures to mitigate such threats.

Domain-specific programming languages (DSLs) are emerging as an important paradigm for the engineering of large reliable software systems. Modular interpreters are an approach to building off-the-shelf software components that implement fragments of DSLs.We describe an approach to implementing modular interpreters in an object-oriented fashion, using the design pattern of extensible computations. The modular structuring and reuse of DSL implementations has potentially important lessons for object-oriented reuse, because of the highly recursive nature of any non-trivial programming language, and the close semantic relationship between inheritance and recursion.We give paradigmatic examples of the definition of extensible computations in the Java programming language, and consider what extensions would be required for typed object-oriented languages in order to support this approach in a statically type-safe manner.KeywordsDomain-specific programming languagesmodular interpretersmonadsmixin-based inheritanceJava

Biodiversity data exchange depends on established standards, such as Darwin Core, Audiovisual Core, Taxon Concept Schema, etc. Standards provide terms with defined semantic meaning and structures for data exchange. Standards simplify interchange of biodiversity data among government agencies, researchers, and engineers. A notable challenge during such data transfers is the complexity involved in processing data for various uses. Most data exchanges are performed via Comma Separated Value (CSV), Extensible Markup Language (XML), or JavaScript Object Notation (JSON), and need to be parsed and imported into a database before being queryable. For example, to explore the data in a Darwin Core Archive zipped file, it is necessary to decompress it, parse XML-based metadata about the content of the file, parse and read Ecological Metadata Language (EML) to extract provenance metadata, and correctly open the text-delimited files that use a wide variety of character encodings, delimiters, enclosures, and escape characters. All of it requires non-trivial data management and programming skills from users. We propose a paradigm shift toward using queryable SQLite-based files for more straightforward data interchange. This approach would reduce friction in data processing by directly using Structured Query Language (SQL). SQLite is an open source, high performance, lightweight database already installed on most computers (SQLite 2024). It integrates a database into a single file, facilitating straightforward data exchange and compression. Its robust engine is able to manage terabytes of data. The SQLite developers are committed to maintaining backward compatibility of both binary and SQL text file versions until 2050 (SQLite 2024). Connectivity to SQLite databases is supported by all popular programming languages. Furthermore, the United States Library of Congress endorses SQLite alongside XML and JSON for data archives, attesting to its long-term reliability (United States Library of Congress 2024). We at the Species File Group are experimenting with using SQLite to create a universal data converter, in which an SQLite database serves as an intermediate data storage format. This provides several useful advantages: 1. Universal data converter By learning a standard SQL schema for biodiversity data, users are able to efficiently write importers and exporters to/from the intermediate schema for a variety of other biodiversity data-exchange formats. Any programming language can be used to develop the conversion scripts. Import and export are completely decoupled from each other, allowing mixing and matching of converters in a flexible way. For example, we aim to use SQLite as an intermediary archive to convert data between Darwin Core Archives, Catalogue of Life Data Package (ColDP), Global Names Verifier, and TaxonWorks (Fig. 1). 2. Semantic versioning of the archive Like any other data format, the intermediate data exchange SQLite schema will evolve over time. Using Git tags and branches, it is straightforward to follow a semantic versioning paradigm (Semantic Versioning 2024). For example, in "v1.2.5" the numbers respectively indicate major, minor, and patch versions. Major versions include substantial changes that might not be backwards compatible, while minor versions are backwards compatible and patch versions indicate tiny bug fixes. 3. Data querying With SQLite-based archives being relational databases, they can immediately be queried without any additional parsing and importing. This allows the archives to be explored with ease by anyone familiar with SQL. The Carpentries also provide online learning resources and courses for researchers to learn SQLite (Martinez and Poisot 2017). 4. Biodiversity application development As SQLite-based archives are relational databases, and the intermediate data exchange schema is standardized and stable, a database file can be used directly by a variety of applications. For example, it would be possible to develop a web application frontend that utilizes the SQLite archive as a backend. It could be possible to develop an ecosystem of biodiversity informatics applications based on SQLite archives. We believe that adopting SQLite for biodiversity data exchange can reduce operational friction, enhance data accessibility, and promise a streamlined and universally compatible data management framework.

This book provides an introduction to R programming and a summary of financial mathematics. It is not always easy for graduate students to grasp an overview of the theory of finance in an abstract form. For newcomers to the finance industry, it is not always obvious how to apply the abstract theory to the real financial data they encounter. Introducing finance theory alongside numerical applications makes it easier to grasp the subject. Popular programming languages like C++, which are used in many financial applications are meant for general-purpose requirements. They are good for implementing large-scale distributed systems for simultaneously valuing many financial contracts, but they are not as suitable for small-scale ad-hoc analysis or exploration of financial data. The R programming language overcomes this problem. R can be used for numerical applications including statistical analysis, time series analysis, numerical methods for pricing financial contracts, etc. This book provides an overview of financial mathematics with numerous examples numerically illustrated using the R programming language.

With the growing usage and migration of interactive applications and systems into today’s environment, the significance of the graphical user interfaces (GUIs) increases as they act as the gates into using systems efficiently. Therefore, extensive efforts are spent to enhance the usability of the GUIs. However, most of the works focus on testing the functional properties of applications’ GUIs rather than nonfunctional features such as performance. For this reason, it is worthwhile to assess the performance of various GUI components at runtime. In this paper, the most popular programming languages used to create GUI based applications namely, Java and C# were compared experimentally to evaluate their performance in terms of the creation and manipulation of GUI components/controls. The experimental results of this study, which is based on 32 testing scenarios, showed that Java outperformed C# in all test scenarios. This might be because of the “HotSpot” performance engine that Java uses. This study is useful for developers to get insights into the performance of different GUI components provided by different programming languages. Also, it helps them to choose the right programming language for their GUI based applications hence enhance the overall applications’ performance and user satisfaction.

Static analyzers have become increasingly popular both as developer tools and as subjects of empirical studies. Whereas static analysis tools exist for disparate programming languages, the bulk of the empirical research has focused on the popular Java programming language. In this paper, we investigate to what extent some known results about using static analyzers for Java change when considering C#-another popular object-oriented language. To this end, we combine two replications of previous Java studies. First, we study which static analysis tools are most widely used among C# developers, and which warnings are more commonly reported by these tools on open-source C# projects. Second, we develop and empirically evaluate EagleRepair: a technique to automatically fix code in response to static analysis warnings; this is a replication of our previous work for Java [20]. Our replication indicates, among other things, that 1) static code analysis is fairly popular among C# developers too; 2) Re-Sharper is the most widely used static analyzer for C#; 3) several static analysis rules are commonly violated in both Java and C# projects; 4) automatically generating fixes to static code analysis warnings with good precision is feasible in C#. The EagleRepair tool developed for this research is available as open source.