A document-like software visualization method for effective cognition of c-based software systems

It is clear that maintenance is a crucial and very costly process in a software life cycle. Nowadays there are a lot of software systems particularly legacy systems that are always maintained from time to time as new requirements arise. One important source to understand a software system before it is being maintained is through the documentation, particularly system documentation. Unfortunately, not all software systems developed or maintained are accompanied with their reliable and updated documents. In this case, source codes will be the only reliable source for programmers. A number of studies have been carried out in order to assist cognition based on source codes. One way is through tool automation via reverse engineering technique in which source codes will be parsed and the information extracted will be visualized using certain visualization methods. Most software visualization methods use graph as the main element to represent extracted software artifacts. Nevertheless, current methods tend to produce more complicated graphs and do not grant an explicit, document-like re-documentation environment. Hence, this thesis proposes a document-like software visualization method called DocLike Modularized Graph (DMG). The method is realized in a prototype tool named DocLike Viewer that targets on C-based software systems. The main contribution of the DMG method is to provide an explicit structural re-document mechanism in the software visualization tool. Besides, the DMG method provides more level of information abstractions via less complex graph that include inter-module dependencies, inter-program dependencies, procedural abstraction and also parameter passing. The DMG method was empirically evaluated based on the Goal/Question/Metric (GQM) paradigm and the findings depict that the method can improve productivity and quality in the aspect of cognition or program comprehension. A usability study was also conducted and DocLike Viewer had the most positive responses from the software practitioners

The Stores Model of Code Cognition

Program comprehension is perhaps one of the oldest topics within the psychology of programming. It addresses a central issue: how programmers work with and manipulate source code to construct effective software systems. Models can play an important role in understanding the challenges developers and engineers contend with. This paper presents a model of program comprehension, or code cognition, which has been derived from literature found within the disciplines of computing and psychology. Drawing on direct experimentation, this paper argues that a model of code cognition should take account of the visual, spatial and linguistic abilities of developers. The strengths and weaknesses of this model are discussed and further research directions presented

A methodology for the capture and analysis of hybrid data: a case study of program debugging

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Enhancing Programming eTextbooks with ChatGPT Generated Counterfactual-Thinking-Inspired Questions

Digital textbooks have become an integral part of everyday learning tasks. In this work, we consider the use of digital textbooks for programming classes. Generally, students struggle with utilizing textbooks on programming to the maximum, with a possible reason being that the example programs provided as illustration of concepts in these textbooks don't offer sufficient interactivity for students, and thereby not sufficiently motivating to explore or understand these programming examples better. In our work, we explore the idea of enhancing the navigability of intelligent textbooks with the use of ``counterfactual'' questions, to make students think critically about these programs and enhance possible program comprehension. Inspired from previous works on nudging students on counter factual thinking, we present the possibility to enhance digital textbooks with questions generated using GPT.Comment: Paper Under Revie

A review and assessment of novice learning tools for problem solving and program development

There is a great demand for the development of novice learning tools to supplement classroom instruction in the areas of problem solving and program development. Research in the area of pedagogy, the psychology of programming, human-computer interaction, and cognition have provided valuable input to the development of new methodologies, paradigms, programming languages, and novice learning tools to answer this demand. Based on the cognitive needs of novices, it is possible to postulate a set of characteristics that should comprise the components an effective novice-learning tool. This thesis will discover these characteristics and provide recommendations for the development of new learning tools. This will be accomplished with a review of the challenges that novices face, an in-depth discussion on modem learning tools and the challenges that they address, and the identification and discussion of the vital characteristics that constitute an effective learning tool based on these tools and personal ideas

Reading skills can predict the programming performance of novices: an eye-tracking study

Due to the character of programming languages, reading ability may have more impact on learning to program than on learning in other subjects. This paper describes an exploratory study of the relationship between reading skills, as perceived through eye tracking, and the ability to program. An empirical investigation into this relationship determined that students with inadequate reading skills are at risk of failing at introductory programming. As an explanation for the effect of reading ability on learning to program, we argue that a programming language is a special high-level written language and that using it requires high levels of comprehension, inferencing, selective attention, organising and reflecting. As a result, a student’s reading ability will have a considerable effect on learning to program. Lack of reading skills may therefore be a factor that affect students’ ability to learn to program. Eye tracking can expose reading skills and, therefore, be used to identify at-risk introductory programming students. The practical contribution of this research is the demonstration of how eye tracking can reveal reading problems among programming students. We relate these reading problems to their programming performance, providing a theoretical account of the connection. The results suggest that efforts to improve reading skills could have a positive impact on learning to program

The development of design guidelines for educational programming environments

Introductory programming courses at university are currently experiencing a significant dropout and failure rate. Whilst several reasons have been attributed to these numbers by researchers, such as cognitive factors and aptitude, it is still unclear why programming is a natural skill for some students and a cause of struggle for others. Most of the research in the computer science literature suggests that methods of teaching programming and students’ learning styles as reasons behind this trend. In addition to the choice of the first programming language taught. With the popularity of virtual learning environments and online courses, several instructors are incorporating these e-learning tools in their lectures in an attempt to increase engagement and achievement. However, many of these strategies fail as they do not use effective teaching practices or recognise the learning preferences exhibited by a diverse student population. Therefore this research proposes that combining multiple teaching methods to accommodate different learners' preferences will significantly improve performance in programming. To test the hypothesis, an interactive web based learning tool to teach Python programming language (PILeT) was developed. The tool’s novel contribution is that it offers a combination of pedagogical methods to support student’s learning style based on the Felder-Silverman model. First, PILeT was evaluated by both expert and representative users to detect any usability or interface design issues that might interfere with students’ learning. Once the problems were detected and fixed, PILeT was evaluated again to measure the learning outcomes that resulted from its use. The experimental results show that PILeT has a positive impact on students learning programming