Dead Code Should Be Buried

2015-09-04 · Source: Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The article "Dead Code Should Be Buried" highlights a significant maintenance challenge within Natural Language Processing (NLP) libraries: the failure to regularly remove outdated or "dead" code. This issue stems from academics' reluctance to make editorial decisions about what content to discard, leading to libraries that struggle to keep pace with the rapid advancements in NLP. The author contends that this accumulation of dead code is detrimental and explains that the spaCy library was developed specifically to address this problem by adopting a different approach to code maintenance and deprecation.

Key takeaway

For NLP Engineers or library maintainers evaluating or developing new tools, recognize that active code deprecation is vital for long-term library health. Your selection criteria should include a library's demonstrated commitment to removing dead code, as this directly impacts its ability to adapt to rapid NLP advancements. Prioritize frameworks that embrace editorial decisions to ensure your projects rely on current, efficient, and maintainable resources.

Key insights

NLP library maintenance suffers from academic reluctance to deprecate dead code, hindering progress.

Principles

• Aggressive deprecation improves libraries.
• Editorial decisions are crucial for NLP library health.

Topics

• Natural Language Processing
• Code Maintenance
• Library Management
• spaCy
• Software Deprecation

Best for: NLP Engineer, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.