An official website of the United States government
Trigger-action programming (TAP) is a programming model enabling users to connect services and devices by writing if-then rules. As such systems are deployed in increasingly complex scenarios, users must be able to identify programming bugs and reason about how to fix them. We first systematize the temporal paradigms through which TAP systems could express rules. We then identify ten classes of TAP programming bugs related to control flow, timing, and inaccurate user expectations. We report on a 153-participant online study where participants were assigned to a temporal paradigm and shown a series of pre-written TAP rules. Half of the rules exhibited bugs from our ten bug classes. For most of the bug classes, we found that the presence of a bug made it harder for participants to correctly predict the behavior of the rule. Our findings suggest directions for better supporting end-user programmers.
more »
« less
Epistemological Pluralism for Diversifying Preservice Early Childhood Teachers’ Programming Experience
Research indicates that computer programming in a bricolage manner is equally strong as structure programming. In this study, we investigated how and why 26 preservice, early childhood teachers learning to program employed diverse approaches to programming. Data included classroom videos, interviews, written reflections, submitted code, and questionnaires. Analysis involved open and axial coding. Findings included (a) all tinkered through trial and error but this does not mean that analytical means were never used, (b) divide-and-conquer was practiced, (c) analytical means were often used in locating the bug whereas tinkering was used mostly in fixing the bug, (d) unnoticing when/where to tinker compromised the programming goal, and (e) robot programming was perceived as creative, artistic, and playful.
more »
« less
- Award ID(s):
- 1906059
- PAR ID:
- 10178617
- Date Published:
- Journal Name:
- Annual meeting program American Educational Research Association
- ISSN:
- 0163-9676
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Bug report reproduction is an important, but time-consuming task carried out during mobile app maintenance. To accelerate this task, current research has proposed automated reproduction techniques that rely on a guided dynamic exploration of the app to match bug report steps with UI events in a mobile app. However, these techniques struggle to find the correct match when the bug reports have missing or inaccurately described steps. To address these limitations, we propose a new bug report reproduction technique that uses an app’s UI model to perform a global search across all possible matches between steps and UI actions and identify the most likely match while accounting for the possibility of missing or inaccurate steps. To do this, our approach redefines the bug report reproduction process as a Markov model and finds the best paths through the model using a dynamic programming based technique. We conducted an empirical evaluation on 72 real-world bug reports. Our approach achieved a 94% reproduction rate on the total bug reports and a 93% reproduction rate on bug reports with missing steps, significantly outperforming the state-of-the-art approaches. Our approach was also more effective in finding the matches from the steps to UI events than the state-of-the-art approaches.more » « less
-
null (Ed.)For several years, the software engineering research community used eye trackers to study program comprehension, bug localization, pair programming, and other software engineering tasks. Eye trackers provide researchers with insights on software engineers’ cognitive processes, data that can augment those acquired through other means, such as on-line surveys and questionnaires. While there are many ways to take advantage of eye trackers, advancing their use requires defining standards for experimental design, execution, and reporting. We begin by presenting the foundations of eye tracking to provide context and perspective. Based on previous surveys of eye tracking for programming and software engineering tasks and our collective, extensive experience with eye trackers, we discuss when and why researchers should use eye trackers as well as how they should use them. We compile a list of typical use cases—real and anticipated—of eye trackers, as well as metrics, visualizations, and statistical analyses to analyze and report eye-tracking data. We also discuss the pragmatics of eye tracking studies. Finally, we offer lessons learned about using eye trackers to study software engineering tasks. This paper is intended to be a one-stop resource for researchers interested in designing, executing, and reporting eye tracking studies of software engineering tasks.more » « less
-
Graph analytics codes are widely used and tend to exhibit input-dependent behavior, making them particularly interesting for software verification and validation. This paper presents Indigo3, a labeled benchmark suite based on 7 graph algorithms that are implemented in different styles, including versions with deliberately planted bugs. We systematically combine 13 sets of implementation styles and 15 common bug types to create the 41,790 CUDA, OpenMP, and parallel C programs in the suite. Each code is labeled with the styles and bugs it incorporates. We used 4 subsets of Indigo3 to test 5 program-verification tools. Our results show that the tools perform quite differently across the bug types and implementation styles, have distinct strengths and weaknesses, and generally struggle with graph codes. We discuss the styles and bugs that tend to be the most challenging as well as the programming patterns that yield false positives.more » « less
-
Deep neural networks (DNNs) are increasingly used in critical applications like autonomous vehicles and medical diagnosis, where accuracy and reliability are crucial. However, debugging DNNs is challenging and expensive, often leading to unpredictable behavior and performance issues. Identifying and diagnosing bugs in DNNs is difficult due to complex and obscure failure symptoms, which are data-driven and compute-intensive. To address this, we propose TransBug a framework that combines transformer models for feature extraction with deep learning models for classification to detect and diagnose bugs in DNNs. We employ a pre-trained transformer model, which has been trained in programming languages, to extract semantic features from both faulty and correct DNN models. We then use these extracted features in a separate deep-learning model to determine whether the code contains bugs. If a bug is detected, the model further classifies the type of bug. By leveraging the powerful feature extraction capabilities of transformers, we capture relevant characteristics from the code, which are then used by a deep learning model to identify and classify various types of bugs. This combination of transformer-based feature extraction and deep learning classification allows our method to accurately link bug symptoms to their causes, enabling developers to take targeted corrective actions. Empirical results show that the TransBug shows an accuracy of 81% for binary classification and 91% for classifying bug types.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
