Does JavaScript software embrace classes?

JavaScript has become one of the most popular programming languages for both client-side and server-side applications. In JavaScript applications, events may be generated, triggered and consumed non-deterministically. Thus, JavaScript applications may suffer from non-deterministic bugs, when events are triggered and consumed in an unexpected order. In this proposal, we aim to characterize and combat non-deterministic bugs in JavaScript applications. Specifically, we first perform a comprehensive study about real-world non-deterministic bugs in server-side JavaScript applications. In order to facilitate bug diagnosis, we further propose approaches to isolate the necessary events that are responsible for the occurrence of a failure. We also plan to design new techniques in detecting non-deterministic bugs in JavaScript applications.

JavaScript is a language that is widely-used for both web- based and standalone applications such as those in the upcoming Windows 8 operating system. Analysis of JavaScript has long been known to be challenging due to its dynamic nature. On top of that, most JavaScript applications rely on large and complex libraries and frameworks, often written in a combination of JavaScript and native code such as C and C++. Stubs have been commonly employed as a partial specification mechanism to address the library problem; however, they are tedious to write, incomplete, and occasionally incorrect. However, the manner in which library code is used within applications often sheds light on what library APIs return or consume as parameters. In this paper, we propose a technique which combines pointer analysis with use analysis to handle many challenges posed by large JavaScript libraries. Our approach enables a variety of applications, ranging from call graph discovery to auto-complete to supporting runtime optimizations. Our techniques have been implemented and empirically validated on a set of 25 Windows 8 JavaScript applications, averaging 1,587 lines of code, demonstrating a combination of scalability and precision.

JavaScript (JS) is one of the most popular programming languages for developing client-side applications mainly due to allowing the adoption of different programming styles, not having strict syntax rules, and supporting a plethora of frameworks. The flexibility that the language provides may accelerate the development of application, but also pose threats to the quality of the final software product, e.g., introducing Technical Debt (TD). TD reflects the additional cost of software maintenance activities to implement new features, occurring due to poorly developed solutions. Being able to forecast the levels of TD in the future can be extremely valuable in managing TD, since it can contribute to informed decision making when designating future repayments and refactoring budget among a company's projects. Despite the popularity of JS and the undoubtful benefits of accurate TD forecasting, in the literature, there is available only a limited number of tools and methodologies that are able to: (a) forecast TD during software evolution, (b) provide a ground-truth TD quantifications to train forecasting, since TD tools that are available are based on different rulesets and none is recognized as a state-of-the-art solution, (c) take into consideration the language-specific characteristics of JS. As a main contribution for this study, we propose a methodology (along with a supporting tool) that supports the aforementioned goals based on the Backward Stepwise Regression and Auto-Regressive Integrated Moving Average (ARIMA). We evaluate the proposed approach through a case study on 19,636 releases of 105 open-source applications. The results point out that: (a) the proposed model can lead to an accurate prediction of TD, and (b) the Number of appearances of the “new” and “eval” keyword along with the number of “anonymous” and “arrow” functions are among the features of JavaScript language that are related to high levels of TD.

2 - The VBA Design Environment

Surveying LinkedIn Profiles of Data Scientists: The Case of the Philippines

Genetic programming and data structures: Geneticprogramming + datastructures = automaticprogramming: By W. B. Langdon. Kluwer Academic Publishers, Boston, MA. (1998). 278 pages. $125.00. NLG 285.00, GBP 85.00

In Engineering and Scientific computing, a significant number of software systems that are being commonly used by the Engineering community are developed and deployed as Open Source Systems (OSS). This collaborative development model has been widely accepted in both academia and industry. As these systems are typically developed by developers who are experts in their fields (e.g., Engineering), some of those programmers may be lacking an essential skillset today's software quality and engineering concepts (e.g., source code maintainability and analyzability, source code vulnerability, parallelization, etc.). An empirical study is presented that examines some of the source code quality aspects and programming practices (e.g., jumps statements, usage of recursion) from software engineering perspective in ten open source Engineering software systems. The studied systems are written in the C/C++ programming languages and comprised of over ten million lines of code, and present numerous tasks of the engineering community, including computer-aided design, computational fluid dynamics, and finite element analysis. Static analysis methods are applied to each system to determine the number of jump statements (Goto and Break statements) and functions that are recursively invoked. Functions that contain direct or indirect recursive calls are determined and counted. The results show that number of the used jump statements and functions that have recursive calls are significant and thus pose more of a roadblock to effectively and automatically maintain, comprehend, analyze, and re-engineer systems. Lastly, this leads to inefficiencies in a software engineer's ability to accomplish these common tasks. The study showed that issues in source code stemming from the lack of coding standards have increased over time and are prevalent.

One of the most fundamental and most highly debated questions in Information Systems education is the role of programming in undergraduate IS programs. On one hand, there are strong arguments and precedents supporting not only the inclusion of programming in these programs but also a significant role for it. For example, both the most recent model curriculum (IS 2002) and many existing undergraduate IS programs include a significant programming component. IS 2002 incorporates a programming course with a highly complex name reflecting the ambiguity about the focus of the course ("Programming, Data, File, and Object Structures,") but it also includes two additional courses that have significant programming components ("Physical Design and Implementation with DBMS" and "Physical Design and Implementation in Emerging Environments"). The "Analysis and Logical Design" course is directly connected to application development/programming. A closer evaluation of the course descriptions reveals a very ambitious agenda; the topics include items such as "programming in traditional and visual environments that incorporate event-driven, object-oriented design," "algorithm development," "program correctness, verification, and validation," "structured and object design approaches," and "structured, event driven, and object oriented application design." This choice of wording, which is practically unchanged from IS'97, signifies a strong commitment to developing the students' programming and software development capabilities. In addition, the general criteria for accrediting undergraduate computing programs (including those in information systems) clearly include programming activities (3 (c)) "The program enables students to achieve, by the time of graduation, ... an ability to design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs.

The COVID-19 pandemic has caused the suspension of face-to-face classes; therefore, greater importance has been given to virtual education. For this reason, it is important to review the new situations in the programming teaching process. This paper aims to interpret the experiences lived by teachers in in the subjects of Fundamentals of Programming and Data Structure in the implementation of virtual education in times of COVID-19 in the Technological Institutes Decentralized from the State of Zacatecas, for this reason, the quantitative, non-experimental research was used. The instrument used was a digital survey addressed to teachers from the Technological Institutes. According to the results obtained, the main problems during virtual education were the lack of connection by geographical location and the lack of adaptation to virtual education. Teaching virtual programming was a good pedagogical experience, the teachers expressed that they gave personalized attention in small groups; however, they mentioned that it was difficult to measure the student’s progress, and the subject contents were not seen with the required degree of complexity. For teaching programming, teachers used platforms such as Moodle, Classroom and Microsoft Teams, virtual classrooms such as Meet and Zoom and digital resources such as videos, presentations and practical manuals. The most important challenges are to have the infrastructure required for the implementation of virtual classes, and the responsibility, commitment and initiative by teachers and students.

Development of a course and textbook focusing on implementation of a spreadsheet program is proposed. The semester-long course will be suitable as a senior elective or graduate course for students with a background in C programming, Unix, and data structures. It will bring together aspects of compiler construction, parser design using Flex and Bison, parse trees, linked lists, function tables, function callbacks, symbol tables, and functional programming. The emphasis will be on the computational internals, so the spreadsheet will operate in ldquobatchrdquo mode, with simple text output, and a separate backend processor will be used to create a web-based GUI.

In the computing education community, there are common understandings regarding what topics should be covered in some specific subjects and their corresponding courses and the preferred sequence in which the topics is presented. Published textbooks written by university educators are indications of such practices. Frequently, developing a new course needs to select a relatively "better" textbook from a large number of similar textbooks based on criteria such as readability and clarity. Majority of the textbooks present the conceptual topics for different aspects of their intended subjects in a progressive manner. That is, the concept of a topic is discussed, coupled with several small examples, and followed by some small exercises such as end of chapter problems. In computing courses, often a term project is assigned to students with the expectation that they will apply the knowledge learned in different topics to one place to solve a problem of relatively large scale. Computing courses such as object-oriented programming and data structures can both be taught using this approach. The author names this the "topic-based approach". However, in mobile programming courses, the ultimate goal is to empower the students with skills for application development. This gives rise to the so-called "app-driven" approach as mentioned in some books. The author had the opportunity to teach a mobile programming course for Android application development recently. This paper introduces how the author prepared the course and is a reflection on the use of the "app-driven" approach in teaching the mobile programming course. Comparisons are made to map out the pros and cons of the more common "topic-based approach" and the "app-driven approach".

At present, the most effective teaching tool is the interactive visualization platform, which directly or indirectly promotes the development of the education field. In addition, the development of this technology will affect many existing fields. The purpose of visual education is to help learners grasp relevant concepts in teaching more clearly. At the same time, visualization technology has a wide range of applications in various teaching fields of computer technology. In programming and data structure courses, the use of visualization teaching platform can deepen the understanding of learners. Based on the visualization information technology, this paper makes an in-depth study on the grading model of minority students in colleges and universities. This paper designs a scoring model by using visualization technology, and uses the model to collect teachers’ scoring results of various indicators of ethnic minority students in colleges and universities, and finally analyses the data.

Programming and Data Structures

Students are facing various difficulties in courses like Programming and Data Structure through undergraduate programs, which is why failure rates and dropouts in these courses are high. Identifying students at risk of failure at an early stage of a semester is a serious challenge in higher education, so predicting students' academic performance is one of the most essential research topics to reduce failure rates, and to improve the performance of students by the end of a semester. We are developing a predictive model based on a deep artificial neural network to predict students' academic performance of upcoming courses based on their grades in previous courses of the first academic year. We have used one of the most common resampling methods, which is the SMOTE approach to handle the problem of the imbalanced dataset. The preliminary results show that our proposed model has achieved 86% accuracy. To put our system in context, we compared our results to some traditional machine learning techniques such as Decision Tree, K-Nearest Neighbor, and Random Forest, which were applied to an online dataset that has been widely used in some previous works. The comparison showed that our experimental results have achieved better results than the other traditional models. In future work, we will use a larger dataset to improve the accuracy of our models. Using other oversampling techniques such as SVM-SMOTE and Random Over Sampler will be used to evaluate their performance in comparison to the algorithms used in our work.

The creation of a new modeling and simulation engineering program presented the opportunity to evaluate the core skills desirable in a well-rounded graduate. Software was identified as an often neglected aspect of modeling and simulation programs and an attempt was made to remedy this. This paper discusses the software skills identified as necessary/desirable in a graduate. The focus is on discrete event simulation (DES), though the skills are transferable to other paradigms. The discussion is partitioned into discussing skills appropriate for simulation application development and simulation tool development. The discussion is further partitioned to discuss core computer science skills (object-oriented programming and data structures), software architecture, graphics development, implementing DES worldviews, and an ability to work with open source software. The result is a graduate that is desirable to industry and graduate research.