Methodology: The Calculus of 'Deaditude'

1. Introduction

Determining whether a technology is "dead" requires moving beyond subjective developer opinions, Reddit flamewars, and Hacker News echo chambers into empirical data analysis. Our engine aggregates multiple signals to compute a unified score so you don't have to rely on that one guy who says "X is dying" every week.

2. Data Sources

GitHub Activity: Stars, forks, commit frequency, and issue resolution time (or lack thereof).
NPM Registry: Weekly download velocity and dependency inclusion rates.
StackOverflow: Question volume and answer latency (the "despair index").

3. The Deaditude Formula

The core equation relies on a weighted average of normalized metric decay over time. Given a time $t$, the Deaditude $D(t)$ is expressed as:

D(t) = α(1 - G_v) + β(1 - N_v) + γ(S_v)

Where:
$G_v$ = Normalized GitHub Velocity
$N_v$ = Normalized NPM Velocity
$S_v$ = StackOverflow Despair Index (Unanswered Questions)

3.1 Mathematical Implementation (Code snippet)

In practice, our Python-based calculation engine adjusts these weights dynamically based on the age of the technology and normalises the variance based on data quality:

def calculate_deaditude_score(
    metrics: Dict[str, Dict[str, float]],
    tech_info: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
    tech_age = _get_tech_age(tech_info)
    weights = YOUNG_TECH_WEIGHTS if tech_age <= YOUNG_TECH_THRESHOLD else BASE_WEIGHTS

    eff_w: Dict[str, float] = {}
    sigmas: Dict[str, float] = {}
    explanations: list[str] = []

    # Effective weights & per-source σ
    for src, base_w in weights.items():
        m = metrics.get(src) or {}
        q = _quality(m)
        eff_w[src] = base_w * q
        sigmas[src] = _sigma_for_quality(q)

    # Renormalise
    total_w = sum(eff_w.values()) or 1.0
    for src in eff_w:
        eff_w[src] /= total_w

    # Weighted mean (0-10)
    overall = sum(
        eff_w[s] * metrics.get(s, {}).get("deaditude_score", 5.0) 
        for s in eff_w
    )
    adjusted_overall = _adjust_score_by_age(overall, tech_age)
    
    # ... calculates Confidence Interval and returns 0-100 score
    pct = adjusted_overall * 10
    return { "overall_score": round(pct, 1) }

3.2 Data Collection Example (GitHub)

We query the GitHub GraphQL API to fetch exact, high-fidelity metrics, defaulting gracefully to REST when rate limited:

def _calculate_deaditude(metrics: Dict[str, Any]) -> float:
    score = BASE_SCORE # Starts at neutral 5.0

    # Negative signals (penalize inactivity)
    commits_30d = metrics.get("commits_last_30d") or 0
    if commits_30d == COMMITS_NONE_THRESHOLD:
        score += COMMITS_NONE_PENALTY
    elif commits_30d < COMMITS_LOW_THRESHOLD:
        score += COMMITS_LOW_PENALTY

    pr_age = metrics.get("pr_age_metrics", {}).get("avg", 0) or 0
    if pr_age > PR_OLD_THRESHOLD:
        score += PR_OLD_PENALTY

    # Positive signals (reward popularity)
    stars = metrics.get("stars", 0)
    if stars > STARS_VERY_HIGH_THRESHOLD:
        score -= STARS_VERY_HIGH_BONUS

    return max(0, min(10, score))

4. Peer Review