Software Engineering in the Age of Large Language Models: An Evidence-Informed Playbook for Practitioners

Engineering Practices in Open-Source Agent Frameworks: A Large-Scale Empirical Study

A large-scale empirical study of mainstream open-source agent frameworks from an engineering perspective, providing empirical evidence linking framework design choices to engineering risks and highlighting the need for stronger guidance and support in agent framework development.

Software comprehension in code-centric and model-driven settings: an experimental comparison of models and code

Developers spend a large portion of their time on software comprehension, making it essential to understand how different artifacts – models and code – support this activity. While prior research has primarily explored the use of models as complementary aids for understanding code, limited evidence exists regarding their standalone effectiveness in conveying software’s structural and behavioral aspects. This gap is particularly relevant in contexts such as Model-Driven Engineering (MDE) and Low-Code/No-Code (LCNC) approaches, where models often serve as the primary or sole representation of software. The paper reports on two complementary controlled experiments that investigate how models and code contribute to understanding software structure and behavior. The first experiment compares comprehension when only reverse-engineered UML models or source code were available (code-centric settings), mimicking scenarios in agile or AI-assisted development where models are outdated or absent. The second experiment focuses on model-driven settings, examining whether providing generated code alongside platform-independent models improves comprehension and whether differences in code generators influence performance. The results demonstrate that models and code achieve comparable overall correctness, and thus models alone may be sufficient in model-centric scenarios where access to code is limited or unavailable. The experiments also reveal a consistent structure-behavior comprehension gap, show differences in comprehension across tasks regardless of artifact type, and indicate that adding code to models can both clarify and confuse comprehension depending on context.

LLMs’ reshaping of people, processes, products, and society in software development: a qualitative exploration with early adopters

Interviews with sixteen early-adopter software professionals who integrated LLM-based tools into their day-to-day work in early to mid-2023 offer actionable implications for developers, organizations, educators, and tool designers seeking to integrate LLMs responsibly into professional software practice.

The Specification Paradox: Rethinking Requirements Engineering in the Age of AI

The growing adoption of Large Language Models (LLMs) in Software Engineering has reinforced the expectation that coding activities can be largely automated. However, this perception may represent yet another historical search for a solution capable of eliminating the inherent challenges of software development. This article discusses the transition from a code-centered paradigm to Specification-Driven Development. We argue that artificial intelligence reduces some of the effort associated with writing source code, but it does not eliminate the complexity of developing professional software systems. Instead, it shifts this complexity toward domain understanding, requirements elicitation, specification development, validation, maintenance, and software evolution. Building on this perspective, we discuss the renewed centrality of Requirements Engineering, considering its implications for productivity and software quality, as well as risks associated with automation bias, ambiguity propagation, Specification Overfitting, and the accumulation of Specification Debt. Finally, we propose the Specification Paradox: the more capable artificial intelligence systems become at automatically generating software, the greater the dependence on correct, complete, verifiable, and explainable human-produced specifications. We conclude that the future of Software Engineering will depend not only on machines'ability to generate code, but also on humans'ability to correctly specify, evaluate, and evolve what is intended to be built.

Prompt engineering applied to code generation: a preliminary systematic review

How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study

This paper is the first to study how SE processes are changing in the development of SE agents and what challenges developers face, and describes a seven-stage workflow and five process shifts, including a move toward evaluation-driven development.