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- research-articleJuly 2024JUST ACCEPTED
A User Study on Explainable Online Reinforcement Learning for Adaptive Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS), Just Accepted https://doi.org/10.1145/3666005Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty because Online RL can leverage data only available at run time. With Deep RL gaining interest, the learned knowledge is no ...
- research-articleJuly 2024
A Quantitative and Qualitative Evaluation of LLM-Based Explainable Fault Localization
Proceedings of the ACM on Software Engineering (PACMSE), Volume 1, Issue FSEArticle No.: 64, Pages 1424–1446https://doi.org/10.1145/3660771Fault Localization (FL), in which a developer seeks to identify which part of the code is malfunctioning and needs to be fixed, is a recurring challenge in debugging. To reduce developer burden, many automated FL techniques have been proposed. However, ...
- research-articleJuly 2024
Verification of Programs with Common Fragments
FSE 2024: Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software EngineeringJuly 2024, Pages 487–491https://doi.org/10.1145/3663529.3663783We introduce a novel verification problem that exploits common code fragments between two programs. We discuss a solution based on Mimicry Monitors that anticipate if the execution of a Program Under Analysis has a counterpart in an Oracle Program ...
- research-articleJune 2024
CrashTalk: Automated Generation of Precise, Human Readable, Descriptions of Software Security Bugs
CODASPY '24: Proceedings of the Fourteenth ACM Conference on Data and Application Security and PrivacyJune 2024, Pages 337–347https://doi.org/10.1145/3626232.3653256Understanding the cause, consequences, and severity of a security bug are critical facets of the overall bug triaging and remediation process. Unfortunately, diagnosing failures is often a laborious process that requires developers to expend significant ...
- posterJune 2024
Automatic detection and correction of code errors applying machine learning - current research state
EASE '24: Proceedings of the 28th International Conference on Evaluation and Assessment in Software EngineeringJune 2024, Pages 456–457https://doi.org/10.1145/3661167.3661198This paper presents an overview of the use of machine learning (ML) algorithms in automatically detecting and correcting errors in code. The main research questions focus on existing approaches, automatic error correction, and challenges related to the ...
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- research-articleJune 2024
Authenticated Range Querying of Historical Blockchain Healthcare Data Using Authenticated Multi-Version Index
Distributed Ledger Technologies: Research and Practice (DLT), Volume 3, Issue 2Article No.: 15, Pages 1–31https://doi.org/10.1145/3624575With growing adoption of blockchain in established and emerging applications, there is an increasing need to support efficient ad hoc querying of authenticated historical data. This is especially true in fields such as healthcare to meet the rigorous ...
- short-paperJune 2024
Demonstration of Udon: Line-by-line Debugging of User-Defined Functions in Data Workflows
SIGMOD/PODS '24: Companion of the 2024 International Conference on Management of DataJune 2024, Pages 476–479https://doi.org/10.1145/3626246.3654756Many big data systems are written in languages such as C, C++, Java, and Scala for high efficiency, whereas data analysts often use Python to conduct data wrangling, statistical analysis, and machine learning. User-defined functions (UDFs) are commonly ...
- short-paperJuly 2024
Contextual visualizations for debugging collaborative robots
RoSE '24: Proceedings of the 2024 ACM/IEEE 6th International Workshop on Robotics Software EngineeringApril 2024, Pages 31–34https://doi.org/10.1145/3643663.3643965Collaborative robots, commonly known as lightweight industrial robots, have become indispensable in manufacturing environments. The growing complexity of work cells necessitates the development of improved techniques, methodologies, and tools for their ...
- research-articleJune 2024
Anonymizing Test Data in Android: Does It Hurt?
AST '24: Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024)April 2024, Pages 88–98https://doi.org/10.1145/3644032.3644463Failure data collected from the field (e.g., failure traces, bug reports, and memory dumps) represent an invaluable source of information for developers who need to reproduce and analyze failures. Unfortunately, field data may include sensitive ...
- research-articleMay 2024
SpotFlow: Tracking Method Calls and States at Runtime
ICSE-Companion '24: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion ProceedingsApril 2024, Pages 35–39https://doi.org/10.1145/3639478.3640029Understanding the runtime behavioral aspects of a software system is fundamental for several software engineering tasks, such as testing and code comprehension. For this purpose, typically, one needs to instrument the system and collect data from its ...
Automated Program Repair, What Is It Good For? Not Absolutely Nothing!
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software EngineeringMay 2024, Article No.: 84, Pages 1–13https://doi.org/10.1145/3597503.3639095Industrial deployments of automated program repair (APR), e.g., at Facebook and Bloomberg, signal a new milestone for this exciting and potentially impactful technology. In these deployments, developers use APR-generated patch suggestions as part of a ...
- research-articleApril 2024Best Paper
SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance
IWOCL '24: Proceedings of the 12th International Workshop on OpenCL and SYCLApril 2024, Article No.: 3, Pages 1–12https://doi.org/10.1145/3648115.3648136The open SYCL standard has established itself as a cross-vendor, cross-platform means to develop software which benefits from GPU and accelerator parallelism. Inherent difficulties in portability between and debuggability of programs for these targets ...
- research-articleMarch 2024
Stump-the-Teacher: Using Student-generated Examples during Explicit Debugging Instruction
SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1March 2024, Pages 653–658https://doi.org/10.1145/3626252.3630934As the number of upper-elementary students (grades 4-7) interested in computer programming increases, there is growing interest in age-appropriate pedagogical approaches to debugging instruction. However, previous research findings with younger novice ...
- research-articleMarch 2024
Failure Artifact Scenarios to Understand High School Students' Growth in Troubleshooting Physical Computing Projects
SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1March 2024, Pages 874–880https://doi.org/10.1145/3626252.3630855Debugging physical computing projects provides a rich context to understand cross-disciplinary problem solving that integrates multiple domains of computing and engineering. Yet understanding and assessing students' learning of debugging remains a ...
- research-articleMarch 2024
dcc --help: Transforming the Role of the Compiler by Generating Context-Aware Error Explanations with Large Language Models
SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1March 2024, Pages 1314–1320https://doi.org/10.1145/3626252.3630822In the challenging field of introductory programming, high enrolments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This paper presents and evaluates the dcc --...
- research-articleMarch 2024
Can Language Models Employ the Socratic Method? Experiments with Code Debugging
SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1March 2024, Pages 53–59https://doi.org/10.1145/3626252.3630799When employing the Socratic method of teaching, instructors guide students toward solving a problem on their own rather than providing the solution directly. While this strategy can substantially improve learning outcomes, it is usually time-consuming ...
- research-articleDecember 2023
Udon: Efficient Debugging of User-Defined Functions in Big Data Systems with Line-by-Line Control
Proceedings of the ACM on Management of Data (PACMMOD), Volume 1, Issue 4Article No.: 225, Pages 1–26https://doi.org/10.1145/3626712Many big data systems are written in languages such as C, C++, Java, and Scala to process large amounts of data efficiently, while data analysts often use Python to conduct data wrangling, statistical analysis, and machine learning. User-defined ...
- research-articleDecember 2023
Always Provide Context: The Effects of Code Context on Programming Error Message Enhancement
CompEd 2023: Proceedings of the ACM Conference on Global Computing Education Vol 1December 2023, Pages 147–153https://doi.org/10.1145/3576882.3617909Programming error messages (PEMs) are notoriously difficult for novice programmers to utilise. Many efforts have been made to enhance PEMs such that they are reworded to explain problems in terms that novices can understand. However, the effectiveness of ...
Semantic Debugging
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software EngineeringNovember 2023, Pages 438–449https://doi.org/10.1145/3611643.3616296Why does my program fail? We present a novel and general technique to automatically determine failure causes and conditions, using logical properties over input elements: “The program fails if and only if int(<length>) > len(<payload>) holds—that is, the ...
- research-articleNovember 2023
DeepDebugger: An Interactive Time-Travelling Debugging Approach for Deep Classifiers
ESEC/FSE 2023: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software EngineeringNovember 2023, Pages 973–985https://doi.org/10.1145/3611643.3616252A deep classifier is usually trained to (i) learn the numeric representation vector of samples and (ii) classify sample representations with learned classification boundaries. Time-travelling visualization, as an explainable AI technique, is designed to ...