Programming assignments automatic grading: review of tools and implementations

Automatic grading of programming assignments is an important topic in academic research. It aims at improving the level of feedback given to students and optimizing the professor time. Several researches have reported the development of software tools to support this process. Then, it is helpfulto get a quickly and good sight about their key features. This paper reviews an ample set of tools forautomatic grading of programming assignments. They are divided in those most important mature tools, which have remarkable features; and those built recently, with new features. The review includes the definition and description of key features e.g. supported languages, used technology, infrastructure, etc. The two kinds of tools allow making a temporal comparative analysis. This analysis infrastructure, etc. The two kinds of tools allow making a temporal comparative analysis. This analysis shows good improvements in this research field, these include security, more language support, plagiarism detection, etc. On the other hand, the lack of a grading model for assignments is identified as an important gap in the reviewed tools. Thus, a characterization of evaluation metrics to grade programming assignments is provided as first step to get a model. Finally new paths in this research field are proposed

On the impact of DSL tools on the maintainability of language implementations

Does the use of DSL tools improve the maintainability of language implementations compared to implementations from scratch? We present empirical results on aspects of maintainability of six implementations of the same DSL using different languages (Java, JavaScript, C#) and DSL tools (ANTLR, OMeta, Microsoft Oslo). Our evaluation indicates that the maintainability of language implementations is indeed higher when constructed using DSL tools

An approach to build JSON-based Domain Specific Languages solutions for web applications

Because of their level of abstraction, Domain-Specific Languages (DSLs) enable building applications that ease software implementation. In the context of web applications, we can find a lot of technologies and programming languages for server-side applications that provide fast, robust, and flexible solutions, whereas those for client-side applications are limited, and mostly restricted to directly use JavaScript, HTML5, CSS3, JSON and XML. This article presents a novel approach to creating DSL-based web applications using JSON grammar (JSON-DSL) for both, the server and client side. The approach includes an evaluation engine, a programming model and an integrated web development environment that support it. The evaluation engine allows the execution of the elements created with the programming model. For its part, the programming model allows the definition and specification of JSON-DSLs, the implementation of JavaScript components, the use of JavaScript templates provided by the engine, the use of link connectors to heterogeneous information sources, and the integration with other widgets, web components and JavaScript frameworks. To validate the strength and capacity of our approach, we have developed four case studies that use the integrated web development environment to apply the programming model and check the results within the evaluation engin

Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study

A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.Additional co-authors: Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Vis

From Monolithic Systems to Microservices: An Assessment Framework

Context. Re-architecting monolithic systems with Microservices-based architecture is a common trend. Various companies are migrating to Microservices for different reasons. However, making such an important decision like re-architecting an entire system must be based on real facts and not only on gut feelings. Objective. The goal of this work is to propose an evidence-based decision support framework for companies that need to migrate to Microservices, based on the analysis of a set of characteristics and metrics they should collect before re-architecting their monolithic system. Method. We designed this study with a mixed-methods approach combining a Systematic Mapping Study with a survey done in the form of interviews with professionals to derive the assessment framework based on Grounded Theory. Results. We identified a set consisting of information and metrics that companies can use to decide whether to migrate to Microservices or not. The proposed assessment framework, based on the aforementioned metrics, could be useful for companies if they need to migrate to Microservices and do not want to run the risk of failing to consider some important information

A Comparative Study of Chisel for FPGA Design

This paper presents the results of a comparative study conducted into designing with the Chisel hardware construction language against the Verilog hardware description language across a range of standard-library and bespoke FPGA design components including an N-bit FIFO, a round-robin arbiter and a complex, scalable arbiter. Comparison metrics such as maximum operating frequency, silicon area, design flow run-time, source-code density and maintainability, simulation run-time and speed of coding are employed to evaluate the merits of designing with Chisel. Each component is implemented with a deep low-level hardware understanding with an aim to evaluate the merits of designing with Chisel from a hardware designers\u27 perspective. The authors discover Chisel\u27s merits for realising synthesizable repetitive designs such as in SoC development, experiencing the benefits of Chisel\u27s object-oriented background in enhancing code maintainability and scalability, and implementation efficiency. However, the authors foresee that Chisel will compliment rather than replace traditional HDLs for RTL design applications due to its limitations in terms of behavioural modelling

Measuring Coverage of Prolog Programs Using Mutation Testing

Testing is an important aspect in professional software development, both to avoid and identify bugs as well as to increase maintainability. However, increasing the number of tests beyond a reasonable amount hinders development progress. To decide on the completeness of a test suite, many approaches to assert test coverage have been suggested. Yet, frameworks for logic programs remain scarce. In this paper, we introduce a framework for Prolog programs measuring test coverage using mutations. We elaborate the main ideas of mutation testing and transfer them to logic programs. To do so, we discuss the usefulness of different mutations in the context of Prolog and empirically evaluate them in a new mutation testing framework on different examples.Comment: 16 pages, Accepted for presentation in WFLP 201

Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT

Context: AI-assisted code generation tools have become increasingly prevalent in software engineering, offering the ability to generate code from natural language prompts or partial code inputs. Notable examples of these tools include GitHub Copilot, Amazon CodeWhisperer, and OpenAI's ChatGPT. Objective: This study aims to compare the performance of these prominent code generation tools in terms of code quality metrics, such as Code Validity, Code Correctness, Code Security, Code Reliability, and Code Maintainability, to identify their strengths and shortcomings. Method: We assess the code generation capabilities of GitHub Copilot, Amazon CodeWhisperer, and ChatGPT using the benchmark HumanEval Dataset. The generated code is then evaluated based on the proposed code quality metrics. Results: Our analysis reveals that the latest versions of ChatGPT, GitHub Copilot, and Amazon CodeWhisperer generate correct code 65.2%, 46.3%, and 31.1% of the time, respectively. In comparison, the newer versions of GitHub CoPilot and Amazon CodeWhisperer showed improvement rates of 18% for GitHub Copilot and 7% for Amazon CodeWhisperer. The average technical debt, considering code smells, was found to be 8.9 minutes for ChatGPT, 9.1 minutes for GitHub Copilot, and 5.6 minutes for Amazon CodeWhisperer. Conclusions: This study highlights the strengths and weaknesses of some of the most popular code generation tools, providing valuable insights for practitioners. By comparing these generators, our results may assist practitioners in selecting the optimal tool for specific tasks, enhancing their decision-making process