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Dharun Anandayuvaraj

dblp:323/4496 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2024
0000-0001-6191-1180ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Debugging and program repair · 88% Empirical software engineering · 12%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Debugging and program repair › software debugging
software failure analysis
1.322024
FAIL: Analyzing Software Failures from the News Using LLMs · ASE 2024
Reflecting on Recurring Failures in IoT Development · ASE 2022
Empirical software engineering
mining software repositories
0.212022
Reflecting on Recurring Failures in IoT Development · ASE 2022

Methods — techniques the papers use, named apart from their topics

large language model · 0.8systematic analysis · 0.6
YearPublicationVenuePosition
2024 FAIL: Analyzing Software Failures from the News Using LLMs
abstract
Software failures inform engineering work, standards, regulations. For example, the Log4J vulnerability brought government and industry attention to evaluating and securing software supply chains. Retrospective failure analysis is thus a valuable line of software engineering research. Accessing private engineering records is difficult, so such analyses tend to use information reported by the news media. However, prior works in this direction have relied on manual analysis. That has limited the scale of their analyses. The community lacks automated support to enable such analyses to consider a wide range of news sources and incidents.
Dharun Anandayuvaraj, Matthew Campbell, Arav Tewari, James C. Davis 0001
ASE1
2022 Reflecting on Recurring Failures in IoT Development
abstract
As IoT systems are given more responsibility and autonomy, they offer greater benefits, but also carry greater risks. We believe this trend invigorates an old challenge of software engineering: how to develop high-risk software-intensive systems safely and securely under market pressures? As a first step, we conducted a systematic analysis of recent IoT failures to identify engineering challenges. We collected and analyzed 22 news reports and studied the sources, impacts, and repair strategies of failures in IoT systems. We observed failure trends both within and across application domains. We also observed that failure themes have persisted over time. To alleviate these trends, we outline a research agenda toward a Failure-Aware Software Development Life Cycle for IoT development. We propose an encyclopedia of failures and an empirical basis for system postmortems, complemented by appropriate automated tools.
Dharun Anandayuvaraj, James C. Davis 0001
ASE1