SE Research is a Complex Ecosystem: Isolated Fixes Keep Failing -- and Systems Thinking Shows Why

• URL: http://arxiv.org/abs/2601.16363v1
• Date: Thu, 22 Jan 2026 23:32:06 GMT
• Title: SE Research is a Complex Ecosystem: Isolated Fixes Keep Failing -- and Systems Thinking Shows Why
• Authors: Mary Shaw, Mary Lou Maher, Keith Webster,
• Abstract summary: The software engineering research community is productive, yet it faces a constellation of challenges.<n>These issues arise from deep structural dynamics within the research ecosystem itself.<n>We sketch such a framework drawing on ideas from complex systems, ecosystems, and theory of change.
• Score: 7.917868855980384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The software engineering research community is productive, yet it faces a constellation of challenges: swamped review processes, metric-driven incentives, distorted publication practices, and increasing pressures from AI, scale, and outright scams. These issues are often treated in isolation, yet they arise from deep structural dynamics within the research ecosystem itself and distract us from the larger role of research in society. Meaningful progress requires a holistic system-level view. We sketch such a framework drawing on ideas from complex systems, ecosystems, and theory of change. Reframing SE's challenges through this lens reveals non-linear feedback loops that sustain current dysfunctions, and it helps to identify leverage points for reform. These are less a matter of isolated fixes and more a matter of exploring coordinated sets of fixes that operate across the SE ecosystem

Related papers

• GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon [62.2138479061386]
  Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities.<n>This paper takes a cross-stack perspective, examining how generative models are being applied from code generation and distributed runtimes through hardware design space exploration to RTL synthesis, physical layout, and verification.
  arXiv  Detail & Related papers  (2026-02-16T22:45:33Z)
• Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science [70.3658845234978]
  Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS)<n>Despite this rapid progress, the field still relies heavily on empirical trial-and-error.<n>This bottleneck stems from the ambiguity of attribution.<n>We propose a factor attribution paradigm to systematically identify collaboration-driving factors.
  arXiv  Detail & Related papers  (2026-02-05T04:19:52Z)
• PACEvolve: Enabling Long-Horizon Progress-Aware Consistent Evolution [64.15555230987222]
  PACEvolve is a framework designed to robustly govern the agent's context and search dynamics.<n>We demonstrate that PACEvolve provides a systematic path to consistent, long-horizon self-improvement.
  arXiv  Detail & Related papers  (2026-01-15T18:25:23Z)
• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• RE-Searcher: Robust Agentic Search with Goal-oriented Planning and Self-reflection [55.125987985864896]
  We present a systematic analysis that quantifies how environmental complexity induces fragile search behaviors.<n>We propose a simple yet effective approach to instantiate a search agent, RE-Searcher.<n>This combination of goal-oriented planning and self-reflection enables RE-Searcher to resist spurious cues in complex search environments.
  arXiv  Detail & Related papers  (2025-09-30T10:25:27Z)
• Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems [92.89673285398521]
  o1-like reasoning systems have demonstrated remarkable capabilities in solving complex reasoning tasks.<n>We introduce an imitate, explore, and self-improve'' framework to train the reasoning model.<n>Our approach achieves competitive performance compared to industry-level reasoning systems.
  arXiv  Detail & Related papers  (2024-12-12T16:20:36Z)
• Chaos Engineering: A Multi-Vocal Literature Review [1.6199400106794553]
  Chaos engineering proactively tests how systems in production behave under turbulent conditions.<n>Traditional resilience testing methods often fail to capture the intricate interactions and failure modes of modern systems.<n>We conducted a Multivocal Literature Review to synthesize insights from both academic and grey literature.
  arXiv  Detail & Related papers  (2024-12-02T11:57:24Z)
• Tackling Erosion in Variant-Rich Software Systems: Challenges and Approaches [0.7373617024876725]
  We conduct an in-depth exploration of the erosion phenomena within variant-rich software systems. We address the current challenges regarding tackling erosion, including issues such as the lack of a consensus on understanding and defining erosion. We outline a first approach aimed at tackling erosion in variant-rich software systems.
  arXiv  Detail & Related papers  (2024-07-04T13:13:45Z)
• A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems [0.0]
  We study the different types of feedback loops in the ML-based decision-making pipeline. We find that the existence of feedback loops can perpetuate, reinforce, or even reduce ML biases.
  arXiv  Detail & Related papers  (2023-05-10T11:15:22Z)
• Navigating the challenges in creating complex data systems: a development philosophy [0.0]
  Perverse incentives and a lack of widespread software engineering skills are among many root causes. We advocate two key development philosophies, namely that one should incrementally grow -- not biphasically plan and build -- DSSs.
  arXiv  Detail & Related papers  (2022-10-21T14:28:53Z)
• On Evaluating Self-Adaptive and Self-Healing Systems using Chaos Engineering [0.6117371161379209]
  We propose CHESS, an approach for the systematic evaluation of self-adaptive and self-healing systems. Chaos engineering is a methodology for subjecting a system to unexpected conditions and scenarios. We investigate the viability of this approach through an exploratory study on a self-healing smart office environment.
  arXiv  Detail & Related papers  (2022-08-28T14:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.