Identifying Code Reading Strategies in Debugging using STA with a Tolerance Algorithm

The purpose of this study was to identify the common code reading strategies of the high and low performing students engaged in a debugging task. Using Scanpath Trend Analysis (STA) with a tolerance on eye tracking data, common scanpaths of high and low performing students were generated. The common scanpaths revealed differences in the code reading patterns and code reading strategies of high and low performing students. High performing students follow a bottom-up code reading strategy when debugging complex programs with logical and semantic errors. A top-down code reading strategy is employed when debugging programs with simple control structures, few lines of code, and simple error types. These results imply that high performing students use flexible debugging strategies based on the program structure. The generated common scanpaths of the low performing students, on the other hand, showed erratic code reading patterns, implying that no obvious code reading strategy was applied. The identified code reading strategies of the high performing students could be explicitly taught to low performing students to help improve their debugging performance

Exploring Common Code Reading Strategies in Debugging

Code reading is a prerequisite of program comprehension which is a fundamental task in software development. Strategies employed on code reading affect the programmer’s success rate of understanding tasks such as debugging. However, there is still limited knowledge about the code reading strategies used by students while performing bug finding task. In this paper, the author describes a summary of her research on novice programmer debugging skills using eye tracking data as a methodology. Eye tracking data were extracted and analyzed using visual effort metrics and sequential analysis of scanpaths using a clustering algorithm to determine common code reading patterns. The author’s research findings revealed differences on the code reading patterns and code reading strategies of high and low performing students. Empirical evaluation on the effectiveness of the strategies used by high performing students was also conducted which suggests that by teaching these strategies to students, improved debugging performance can be observed

Identifying Common Code Reading Patterns using Scanpath Trend Analysis with a Tolerance

Eye tracking data, particularly scanpath, provides valuable insights about code reading patterns which could describe actual comprehension strategies used. However, aggregating multiple scanpaths into one representative path is challenging since individual scanpaths tend to be different and are highly individualistic. The differences may affect the identification of a representative path which could decrease its similarity to individual scanpaths. Thus, we aim to identify a trending scanpath using Scanpath Trend Analysis (STA) with a tolerance to reveal common code reading patterns of high and low performing students while finding bugs in a static source code. Results show that variance exists in the scanpaths of high performing students which suggests that they follow varied code reading patterns while low performing students follow similar code reading patterns. Further, high performing students read code in a logical manner and a somewhat linear code reading pattern along with chunking of program code was employed which makes it possible to perceive the program better and hence, error regions are fixated. In contrast, low performing students jump directly to certain statements without following program\u27s logic. This study addresses the challenge of identifying common code reading patterns that could help us determine effective strategies to be explicitly taught to students and develop learning materials to help improve their code reading and code comprehension skills

Exploring the importance of soft and hard skills as perceived by IT internship students and industry: a gap analysis

The research paper proposes a skills gap methodology that utilized the respondent experiences in the internship program to measure the importance of the Information Technology (IT) skills gap as perceived by IT students and the industry. The questionnaires were formulated based on previous studies, however, was slightly modified, validated and pilot tested to fit into the needs of the research. Respondents of this study were the IT students enrolled in internship while industry partners respondents were the internship supervisors of the IT students in their respected industries. Internship IT students were selected since they have a strong background on the needs of the company based on their internship experience. The findings of this study revealed that teamwork and communication skills are very important soft skills to be possessed by IT graduates as perceived by the respondents. Further, results reveal that there was no significant difference in the perception of the respondents in terms of the importance of soft skills. However, this finding contradicts the results in the case of hard skills were in there were a big range of disagreement on the importance of hard skills. IT students perceived that hard skills were very important while industry perceived hard skills were somewhat important. The study suggests that the university should enrich the soft skills and entry level hard skills component in the curriculu