Debugging Performance Research Articles

A Flipped Systematic Debugging Approach to Enhance Elementary Students’ Program Debugging Performance and Optimize Cognitive Load

Reintroducing computer science (CS) education in K–12 schools to promote computational thinking (CT) has attracted significant attention among scholars and educators. Among the several essential components included in CS and CT education, program debugging is an indispensable skill. However, debugging teaching has often been overlooked in K–12 contexts, and relevant empirical studies are lacking in the literature. Moreover, novices generally have poor performance in domain knowledge and strategic knowledge concerning debugging. They also consistently experience a high cognitive burden in debugging learning. To address these gaps, we developed a flipped systematic debugging approach combined with a systematic debugging process (SDP) and the modeling method. A quasi-experimental study was conducted to explore the effectiveness of this flipped systematic debugging approach, in which 83 fifth-grade students attended the flipped debugging training lessons with the SDP–modeling method, and 75 fifth-grade students attended the unassisted flipped debugging training lessons without the SDP–modeling method. The results indicated that flipped debugging training using the SDP–modeling method improved students’ debugging skills. The results from the questionnaire showed that the proposed teaching approach increased the students’ investment in germane cognitive load by promoting schema construction. It also helped reduce students’ intrinsic and extraneous cognitive load in learning.

Improving the performance of reverse debugging

Reverse debugging is the software development technique that effectively helps fix bugs occurring at the nondeterministic program behavior. It allows one to examine the past states of the program without rerunning it. An implementation of reverse debugging based on deterministic replay in the QEMU 2.0 emulator is described. A number of techniques improving the debugging performance due to reducing the amount of saved data, optimized storage of system snapshots, indexing, and compressing of the event log are proposed. The emulator can work together with the interactive GDB debugger, which makes it possible to use the reverse-continue, reverse-nexti, reverse-stepi and reverse-finish commands in the course of debugging. The execution time of these commands depends on the frequency of recording the system's state snapshots. An estimate of the optimal frequency for the reverse-continue command is obtained.

Do moods affect programmers’ debug performance?

There is much research that shows people’s mood can affect their activities. This paper argues that this also applies to programmers, especially their debugging. Literature-based framework is presented linking programming with various cognitive activities as well as linking cognitive activities with moods. Further, the effect of mood on debugging was tested in two experiments. In the first experiment, programmers (n = 72) saw short movie clips selected for their ability to provoke specific moods. Afterward, they completed a debugging test. Results showed the video clips had a significant effect on programmers’ debugging performance; especially, there was a significant difference after watching low- and high-arousal-evoking video clips. In the second experiment, programmers’ mood was manipulated by asking participants (n = 19) to dry run algorithms for at least 16 min. They performed some physical exercises before continuing dry running algorithms again. The results showed a significant increase in arousal and valence that coincided with an improvement in programmers’ task performance after the physical exercises. Together, this suggests that programmers’ moods influence some programming tasks such as debugging.

Debugging strategies and tactics in a multi-representation software environment

This paper investigates the interplay between high level debugging strategies and low level tactics in the context of a multi-representation software development environment (SDE). It investigates three questions. 1. How do programmers integrate debugging strategies and tactics when working with SDEs? 2. What is the relationship between verbal ability, level of graphical literacy and debugging (task) performance. 3. How do modality and perspective influence debugging strategy and deployment of tactics? The paper extends the work of Katz and Anderson [1988. Debugging: an analysis of bug location strategies. Human-Computer Interaction 3, 359–399] and others in terms of identifying high level debugging strategies, in this case when working with SDEs. It also describes how programmers of different backgrounds and degrees of experience make differential use of the multiple sources of information typically available in a software debugging environment. Individual difference measures considered among the participants were their programming experience and their knowledge of external representation formalisms. The debugging environment enabled the participants, computer science students, to view the execution of a program in steps and provided them with concurrently displayed, adjacent, multiple and linked programming representations. These representations comprised the program code, two visualisations of the program and its output. The two visualisations of the program were available, in either a largely textual format or a largely graphical format so as to track interactions between experience and low level mode-specific tactics, for example. The results suggest that (i) additionally to deploying debugging strategies similar to those reported in the literature, participants also employed a strategy specific to SDEs, following execution, (ii) verbal ability was not correlated with debugging performance, (iii) knowledge of external representation formalisms was as important as programming experience to succeed in the debugging task, and (iv) participants with greater experience of both programming and external representation formalisms, unlike the less experienced, were able to modify their debugging strategies and tactics effectively when working under different format conditions (i.e. when working with either largely graphical or largely textual visualisations) in order to maintain their high debugging accuracy level.

A method for measuring programmer debugging performance from key strokes

AbstractIn software development management, a method is necessary that is capable of measuring programmer performance objectively, and easily. This paper presents a method to measure programmer debugging performance from key strokes. Debugging performance is defined as the efficiency of programmer activities from discovery of software failures to correction of their causes. In this method, programmer activity is first categorized into several classes by monitoring key strokes. Programmer property parameters are then extracted by applying model‐based analysis on monitored programmer activity sequences. The programmer model defined here is based on the assumption that a programmer activity sequence is a Markov process. Finally, several programmer performance values are computed from the extracted parameters. The major value is D, which is the estimated length of time required for debugging one fault. Experiments show that the method is effective enough in practical software development. One of these findings is that D shows strong correlation with a value that represents programmer productivity (the length of program development time for common specifications).