The earliest entries reveal a foundational period where the very concept of debugging was being defined and integrated into computing systems. Initially, the focus was on establishing basic debugging capabilities, often tied to "source language level" (1963, 1964) and creating "online debugging program[s]" (1968). There was an early recognition of design principles to "facilitate debugging" (1968). By the early 1970s, the conversation expanded to "extendible interactive debugging system[s]" and attempts "Toward Automatic Debugging of Low Level Code" (1971). As computing evolved, so did the tools; the mid-to-late 1970s saw debugging tailored for "high level languages" (1973, 1975) and even niche applications like "constructing and debugging manipulator programs" (1976). A notable shift by the end of this period was the recognition of "Real-Time: The 'Lost World' Of Software Debugging and Testing" (1980), indicating a growing awareness of domain-specific challenges.
Tackling Concurrency and Scale: 1981-1990
As systems grew in complexity, the methods for finding and fixing issues had to adapt. The early 1980s saw attention given to "Testing and Debugging Custom Integrated Circuits" (1981), signaling a move beyond pure software. New methodologies emerged, such as "Programmers Use Slices When Debugging" (1982) and the "Wolf Fence' Algorithm" (1982), demonstrating an evolving toolkit. By the mid-1980s, the spotlight was firmly on "Interactive Debugging Environment[s]" (1985) and, significantly, the burgeoning challenge of "Debugging Ada Tasking Programs" (1985) and "Concurrent Programs" (1989) and "distributed programs" (1986, 1987, 1989, 1990). This era also introduced "Knowledge-Based Program Debugging Systems" (1987) and "Automatic runtime consistency checking" (1989), pointing towards more intelligent, automated assistance. Furthermore, "Visualizing Performance Debugging" (1989) indicated an early recognition of the power of visual aids for complex systems.
This period saw a diversification and deepening of debugging techniques, alongside a growing awareness of the human element. The early 1990s introduced concepts like "An Execution-Backtracking Approach to Debugging" (1991) and "Two-Dimensional Pinpointing: Debugging with Formal Specifications" (1991), highlighting more rigorous and systematic methods. The challenges of "Multiprocessor performance debugging" (1992) continued to be a focus. A significant cultural and technical debate, "The Debugging Scandal and What to Do About It" (1997), emerged, suggesting a widespread recognition of debugging's persistent difficulties. Visualization became more prominent with "Graphical debugging" (1993) and "Software Visualization for Debugging" (1997). The late 1990s and early 2000s also explored "dynamic slicing for debugging distributed programs" (1998) and debated "Automated Debugging: Are We Close" (2001), indicating a persistent aspiration for greater automation. "Message Logging Paradigm for Masking Process Crashes" (1996) showcased logging's role in fault tolerance.
Distributed Systems, Specialized Hardware, and New Analytics: 2002-2011
As systems became increasingly distributed and specialized, debugging efforts followed suit. Visual debugging remained a theme (2002), and concepts like "Black Box Debugging" (2003) and "Debugging in an Asynchronous World" (2003) emerged, reflecting the challenges of understanding opaque or complex interactions. Hardware-assisted debugging gained traction with "iWatcher: Simple, General Architectural Support for Software Debugging" (2004). A significant trend was the application of statistical methods, exemplified by "Statistical Debugging and Automated Program Failure Triage" (2007). The focus on "Debugging Devices" (2008) and "Automating Postsilicon Debugging and Repair" (2008) showed growing interest in hardware lifecycle. Distributed systems debugging became a dominant theme, with titles like "Live debugging of distributed systems" (2008) and "Debugging Large Scale Applications With Virtualization" (2010, 2011). The rise of web services also brought "Logging in the Age of Web Services" (2009) into focus, showing how logging was becoming critical for operational visibility.
The Era of Ubiquitous and Cloud Computing: 2012-2017
This period is marked by debugging challenges arising from the proliferation of mobile, embedded, and cloud-based systems. "Debugging abnormal battery drain on smartphones" (2012) and "Energy debugging in smartphones" (2012, 2017) highlighted new, resource-specific concerns. The Internet of Things (IoT) explicitly entered the discussion with "Debugging the Internet of Things" (2015), often tied to "Wireless Sensor Networks" (2013, 2015). Cloud computing architectures began to feature prominently with "Troubleshooting & Debugging Microservices in Kubernetes" (2018, though a 2017 title also mentions "Debugging Distributed Systems"). There was a sustained interest in "deterministic replay" (2012, 2013) for complex systems. Formal methods continued to be applied, for instance, in "Formal methods for automated debugging" (2017). A shift towards human factors was also apparent with titles like "The Debugging Mindset" (2017) and "Debugging Under Fire: Keep your Head when Systems have Lost their Mind" (2017), emphasizing soft skills and resilience.
AI/ML and Cloud-Native Convergence: 2018-2021
This era saw the maturation of cloud-native debugging and the significant emergence of challenges related to Artificial Intelligence and Machine Learning. Debugging "Microservices in Kubernetes" (2018, 2019, 2020) became a recurring theme, often involving "Live Kubernetes Debugging with the Elastic Stack" (2019). The complexity of "Big Data Analytics" also prompted specific debugging approaches (2020). Crucially, the need to debug AI and ML models started to appear explicitly, with titles like "Debugging of Behavioural Models using Counterexample Analysis" (2018), "Interactive AI Model Debugging and Correction" (2022, though published in 2022, the trend started here), and "Federated knowledge base debugging in DL-Lite" (2021). The hardware aspect continued to evolve, with "Post-silicon Validation and Debug" (2019) and "Facilitating analog circuit design and debugging" (2020). There was also an emphasis on "Developer-Centric Automated Debugging" (2021), focusing on practical, user-oriented solutions.
The AI/ML & Observability Frontier: 2022-2025
The most recent period is characterized by the dominance of AI/ML debugging and the rise of "Observability" as a distinct discipline. "Debugging Machine Learning Models" (2022) became a central focus, addressing challenges like "Understanding Deep Learning Optimization" (2022), "Explaining and Interactively Debugging Deep Models" (2022), and "Data Systems for Managing and Debugging Machine Learning Workflows" (2022). This culminated in the critical need for "reliability and interactive debugging for large language models" (2024), highlighting the frontier of AI debugging. The concept of "Observability 2.0: Transforming Logging & Metrics" (2023, 2024) emerged as a key strategy, integrating logging and metrics for deeper system understanding. Interactive and visual debugging continued to evolve, with "Interactive Debugging Approach Based on Time-traveling Queries" (2023) and "Visual, Interactive Deep Model Debugging" (2024). Looking ahead to 2025, the challenges extend to "Cloud-Scale Debugging" (2024, 2025) and "A Test, Debug, and Silicon Lifecycle Management Architecture for a UCIe-Based Open Chiplet Ecosystem" (2025), indicating a move towards integrated debugging solutions across diverse and highly complex hardware and software ecosystems. The upcoming "Debugging Book" (2025) suggests a growing maturity and consolidation of knowledge in this field.
