Automated Programming Assessment Systems (APAS) are used for overcoming problems associated with manually managed programming assignments, such as objective and efficient assessing in large classes and providing timely and helpful feedback. In this paper we survey the literature and software in this field and identify the set of necessary features that make APAS comprehensive - such that it can support all key stages in the assessment process. Put differently, comprehensive APAS is generic enough to meet the demands of “any” computer science course. Despite the vast number of publications, the choice of software turns out to be very limited. We contribute by developing Edgar, a comprehensive open-source APAS which, to the best of our knowledge, exceeds any other similar free and/or open-source tool. Edgar is the result of three years of development and usage in, for the time being, eight courses dealing with various programming languages and paradigms (C, Java, SQL, etc.). Edgar supports various text-based programming languages, multi-correct multiple-choice questions, provides rich exam logging and monitoring infrastructure to prevent potential fraudulent behaviour, and subsequent data analysis and visualization of students' scores, exams, question quality, etc. It can be deployed on all major operating systems and is written in a modular fashion so that it can be adjusted and scaled to a custom fit. We comment on the architecture and present data from real-world use-cases to support these claims. Edgar is in active use today (1000+ students per semester) and it is being constantly developed with new features.

Online shopping, already on a steady rise, was propelled even further with the advent of the COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online. The credit card fraud detection problem has become relevant more than ever as the losses due to fraud accumulate. Most research on this topic takes an isolated, focused view of the problem, typically concentrating on tuning the data mining models. We noticed a significant gap between the academic research findings and the rightfully conservative businesses, which are careful when adopting new, especially black-box, models. In this paper, we took a broader perspective and considered this problem from both the academic and the business angle: we detected challenges in the fraud detection problem such as feature engineering and unbalanced datasets and distinguished between more and less lucrative areas to invest in when upgrading fraud detection systems. Our findings are based on the real-world data of CNP (card not present) fraud transactions, which are a dominant type of fraud transactions. Data were provided by our industrial partner, an international card-processing company. We tested different data mining models and approaches to the outlined challenges and compared them to their existing production systems to trace a cost-effective fraud detection system upgrade path.

The first automated assessment of student programs was reported more than 60 years ago, but this topic remains relevant and highly topical among computer science researchers and teachers. In the last decade, several factors have contributed to the popularity of this approach, such as the development of massive online courses, where large numbers of students can hardly be assessed manually, the COVID-19 pandemic with a strong online presence and physical relocation of students, and the ever-increasing shortage of personnel in the field CS. Modern Automated Programming Assessment Systems (APASs) are nowadays implemented as web applications. For such web applications, especially those that support immediate (on-demand) program assessments and feedback, it can be quite a challenge to implement the various system modules in a secure and scalable manner. Over the past six years, we have developed and actively deployed “Edgar”—a state-of-the-art APAS that enables immediate program evaluation and feedback in any programming language (SQL, C, Java, etc.). In this article, we look at the APAS web application architecture with a focus on scalability issues. We review fundamental features such as dynamic analysis and untrusted code execution, as well as more complex cases such as static analysis and plagiarism detection, and we summarize the lessons learned over the previous six years of research. We identify scalability challenges, show how they have been addressed in APAS Edgar, and then propose general architectural solutions, building blocks and patterns to address those challenges.

A data warehouse should be tested for data quality on regular basis, preferably as a part of each ETL cycle. That way, a certain degree of confidence in the data warehouse reports can be achieved, and it is generally more likely to timely correct potential data errors. In this paper, we present an algorithm primarily intended for integration testing in the data warehouse environment, though more widely applicable. It is a generic, time-constrained, metadata driven algorithm that compares large database tables in order to attain the best global overview of the dataset’s differences in a given time frame. When there is not enough time available, the algorithm is capable of producing coarse, less precise estimates of all datasets differences, and if allowed enough time, the algorithm will pinpoint exact differences. This paper presents the algorithm in detail, presents algorithm evaluation on the real project’s data and TPC-H dataset, and comments on its usability. The tests show that algorithm outperforms the relational engine when the percentage of differences in the database is relatively small, which is typical for data warehouse ETL environments. DOI: http://dx.doi.org/10.5755/j01.itc.47.1.18171