In the world of software development, where code is king and metrics reign supreme, one longstanding measure has come under fresh scrutiny: lines of code, or LoC, as a yardstick for function complexity. Developers have long debated the merits of capping function lengths at arbitrary thresholds—say, 50 or 100 lines—to promote readability and maintainability. But a recent post on Axol’s Blog calls this practice into question, labeling it a hallmark of amateur thinking. The author argues that blindly splitting functions based on LoC ignores deeper principles of clean code, such as logical cohesion and performance efficiency.

This critique resonates amid broader industry conversations about productivity metrics. For years, LoC has been wielded as a blunt instrument in code reviews, with managers and linters enforcing strict limits. Yet, as the blog points out, a lengthy function isn’t inherently problematic if it encapsulates a single, well-defined responsibility. Think of it like a novel: a long chapter can be compelling if it tells a coherent story, but chopping it arbitrarily might dilute its impact.

The Flaws in Counting Lines

Critics of LoC emphasize its superficiality. According to a discussion on Hacker News, where the Axol’s Blog post gained traction, LoC fails to account for code density—dense, expressive languages like Python can achieve more in fewer lines than verbose ones like Java. This metric also overlooks whitespace, comments, and formatting, which can inflate counts without adding value. The post vividly illustrates this by imagining a function bloated with unnecessary breaks, turning elegant logic into a fragmented mess just to satisfy a line limit.

Moreover, LoC encourages counterproductive behaviors. Developers might game the system by extracting trivial sub-functions, leading to a proliferation of tiny, hard-to-follow methods. As noted in a piece from Leadership Loop, this promotes quantity over quality, ignoring the real drivers of software value like defect rates and user satisfaction. Industry insiders echo this, pointing out that in high-stakes environments like financial systems or AI models, forcing splits can introduce bugs through overlooked edge cases.

Beyond LoC: Smarter Alternatives

So, if LoC is “dumb” for functions, as Axol’s Blog asserts, what should replace it? Experts advocate for qualitative metrics, such as cyclomatic complexity, which measures decision paths in code, or cohesion scores that evaluate how tightly related a function’s elements are. A thread on Milestone highlights LoC’s obsolescence, suggesting teams focus on outcomes like deployment frequency and error rates instead.

Practical examples abound. In open-source projects, maintainers often prioritize functions that are long but linear—easy to read from top to bottom—over fragmented ones. The blog post targets those “intimidated by long functions,” urging a mindset shift: evaluate based on comprehensibility, not length. This aligns with insights from Slideshare, which compares LoC to function points, a metric that quantifies user-facing features rather than raw code volume.

Industry Shifts and Future Implications

The backlash against LoC isn’t new, but its persistence in linters and style guides keeps it relevant. A post on Threads dismisses it as useful only for basic linting thresholds, not serious evaluation. As software engineering evolves with AI-assisted coding, metrics like LoC may fade further, replaced by automated tools assessing semantic quality.

For industry veterans, this debate underscores a timeless truth: metrics should serve the code, not dictate it. By moving beyond arbitrary line counts, developers can foster more robust, intuitive systems. As Axol’s Blog concludes, true expertise lies in recognizing when a function’s length enhances rather than hinders its purpose, paving the way for innovation in an era of increasingly complex software demands.