diff --git a/AGGREGATED-PERFORMANCE.md b/AGGREGATED-PERFORMANCE.md index 6508256..7880d15 100644 --- a/AGGREGATED-PERFORMANCE.md +++ b/AGGREGATED-PERFORMANCE.md @@ -1,13 +1,13 @@ # UN Inception: Aggregated Performance Analysis -**Analysis Date:** 1770391601.721544 -**Reports Analyzed:** 4.2.0, 4.2.10, 4.2.11, 4.2.12, 4.2.13, 4.2.14, 4.2.15, 4.2.16, 4.2.17, 4.2.18, 4.2.19, 4.2.20, 4.2.21, 4.2.22, 4.2.23, 4.2.24, 4.2.25, 4.2.26, 4.2.27, 4.2.28, 4.2.29, 4.2.3, 4.2.30, 4.2.31, 4.2.32, 4.2.36, 4.2.37, 4.2.38, 4.2.4, 4.2.46, 4.2.5, 4.2.50, 4.2.51, 4.2.52, 4.2.6, 4.2.7, 4.2.8, 4.2.9, 4.3.0 +**Analysis Date:** 1770551195.2485962 +**Reports Analyzed:** 4.2.0, 4.2.10, 4.2.11, 4.2.12, 4.2.13, 4.2.14, 4.2.15, 4.2.16, 4.2.17, 4.2.18, 4.2.19, 4.2.20, 4.2.21, 4.2.22, 4.2.23, 4.2.24, 4.2.25, 4.2.26, 4.2.27, 4.2.28, 4.2.29, 4.2.3, 4.2.30, 4.2.31, 4.2.32, 4.2.36, 4.2.37, 4.2.38, 4.2.4, 4.2.46, 4.2.5, 4.2.50, 4.2.51, 4.2.52, 4.2.6, 4.2.7, 4.2.8, 4.2.9, 4.3.0, 4.3.1 --- ## Executive Summary -Analysis of 39 performance reports reveals **significant variance** in execution metrics across releases. Different languages rank as slowest/fastest in different runs, indicating **non-deterministic execution patterns** likely caused by: +Analysis of 40 performance reports reveals **significant variance** in execution metrics across releases. Different languages rank as slowest/fastest in different runs, indicating **non-deterministic execution patterns** likely caused by: 1. **Orchestrator placement on CPU-bound pool** (not an SRE best practice) 2. **Resource contention** between the orchestrator & test jobs @@ -61,6 +61,7 @@ Analysis of 39 performance reports reveals **significant variance** in execution | 4.2.8 | 111s | kotlin (313s) | fortran (28s) | -6s (-5.1%) | | 4.2.9 | 107s | ruby (279s) | d (19s) | -4s (-3.6%) | | 4.3.0 | 376s | typescript (829s) | c (47s) | +269s (+251.4%) | +| 4.3.1 | 238s | go (414s) | prolog (58s) | -138s (-36.7%) | **Observation:** Average duration increased **0.0%** from 0s to 0s. @@ -116,6 +117,7 @@ The same language changes dramatically in rank between runs: - 4.2.8: 253s - 4.2.9: 166s - 4.3.0: 196s + - 4.3.1: 164s - **Range:** 32s → 2173s (6690.6% variance) **R:** @@ -158,6 +160,7 @@ The same language changes dramatically in rank between runs: - 4.2.8: 126s - 4.2.9: 54s - 4.3.0: 453s + - 4.3.1: 163s - **Range:** 9s → 1834s (20277.8% variance) **SCHEME:** @@ -200,6 +203,7 @@ The same language changes dramatically in rank between runs: - 4.2.8: 55s - 4.2.9: 153s - 4.3.0: 171s + - 4.3.1: 276s - **Range:** 15s → 1574s (10393.3% variance) **PYTHON:** @@ -242,6 +246,7 @@ The same language changes dramatically in rank between runs: - 4.2.8: 253s - 4.2.9: 165s - 4.3.0: 671s + - 4.3.1: 148s - **Range:** 19s → 1574s (8184.2% variance) **TCL:** @@ -284,6 +289,7 @@ The same language changes dramatically in rank between runs: - 4.2.8: 247s - 4.2.9: 54s - 4.3.0: 476s + - 4.3.1: 132s - **Range:** 20s → 1572s (7760.0% variance) @@ -332,6 +338,7 @@ The same language changes dramatically in rank between runs: 4.2.8: fortran, groovy, crystal, java, powershell 4.2.9: d, julia, csharp, v, objc 4.3.0: c, bash, php, fortran, ruby +4.3.1: prolog, awk, powershell, typescript, dart **Slowest Languages by Run:** @@ -374,6 +381,7 @@ The same language changes dramatically in rank between runs: 4.2.8: kotlin, python, javascript, tcl, raku 4.2.9: ruby, deno, rust, crystal, java 4.3.0: typescript, go, python, java, objc +4.3.1: go, groovy, perl, deno, objc **Conclusion:** No consistent "fast" or "slow" languages across runs. This proves: - Execution order is random or system-dependent @@ -384,9 +392,9 @@ The same language changes dramatically in rank between runs: ### 4. API Health Trends -**Overall API Health:** 7.8/100 (avg across 8 releases) +**Overall API Health:** 6.9/100 (avg across 9 releases) **Trend:** STABLE -**Total Retries (all releases):** 3644 +**Total Retries (all releases):** 4293 | Release | Health Score | Total Retries | 429 (Rate Limit) | 5xx (Server) | Timeout | Connection | |---------|--------------|---------------|------------------|--------------|---------|------------| @@ -398,6 +406,7 @@ The same language changes dramatically in rank between runs: | 4.2.51 | 28/100 | 36 | 0 | 36 | 0 | 0 | | 4.2.52 | 34/100 | 33 | 0 | 33 | 0 | 0 | | 4.3.0 | 0/100 | 876 | 839 | 27 | 10 | 0 | +| 4.3.1 | 0/100 | 649 | 634 | 5 | 10 | 0 | **Interpretation:** - **Score 95-100:** API healthy, tests pass on first attempt @@ -553,26 +562,26 @@ Keep it as-is for stress testing, but in separate test environment. | Language | Min (s) | Max (s) | Avg (s) | Range (s) | Variance % | |----------|---------|---------|---------|-----------|------------| -| DOTNET | 5 | 1539 | 168.1 | 1534 | 30680.0% | -| R | 9 | 1834 | 228.6 | 1825 | 20277.8% | -| NIM | 8 | 1557 | 176.5 | 1549 | 19362.5% | -| CLOJURE | 8 | 1549 | 184.3 | 1541 | 19262.5% | -| FORTRAN | 8 | 1547 | 135.6 | 1539 | 19237.5% | -| PERL | 8 | 1546 | 176.8 | 1538 | 19225.0% | -| D | 8 | 1545 | 139.8 | 1537 | 19212.5% | -| ZIG | 8 | 1542 | 216.4 | 1534 | 19175.0% | -| FORTH | 8 | 1540 | 173.3 | 1532 | 19150.0% | -| KOTLIN | 8 | 1536 | 157.5 | 1528 | 19100.0% | -| CSHARP | 8 | 1534 | 163.1 | 1526 | 19075.0% | -| LUA | 9 | 1548 | 206.9 | 1539 | 17100.0% | -| PROLOG | 9 | 1540 | 125.9 | 1531 | 17011.1% | -| RUST | 9 | 1537 | 188.2 | 1528 | 16977.8% | -| SCHEME | 15 | 1574 | 165.2 | 1559 | 10393.3% | -| OBJC | 17 | 1559 | 163.3 | 1542 | 9070.6% | -| POWERSHELL | 14 | 1269 | 128.6 | 1255 | 8964.3% | -| PYTHON | 19 | 1574 | 175.5 | 1555 | 8184.2% | -| OCAML | 19 | 1537 | 168.6 | 1518 | 7989.5% | -| TCL | 20 | 1572 | 188.5 | 1552 | 7760.0% | +| DOTNET | 5 | 1539 | 173.6 | 1534 | 30680.0% | +| R | 9 | 1834 | 227.0 | 1825 | 20277.8% | +| NIM | 8 | 1557 | 175.9 | 1549 | 19362.5% | +| CLOJURE | 8 | 1549 | 188.4 | 1541 | 19262.5% | +| FORTRAN | 8 | 1547 | 139.2 | 1539 | 19237.5% | +| PERL | 8 | 1546 | 182.1 | 1538 | 19225.0% | +| D | 8 | 1545 | 141.9 | 1537 | 19212.5% | +| ZIG | 8 | 1542 | 218.8 | 1534 | 19175.0% | +| FORTH | 8 | 1540 | 172.7 | 1532 | 19150.0% | +| KOTLIN | 8 | 1536 | 161.9 | 1528 | 19100.0% | +| CSHARP | 8 | 1534 | 162.4 | 1526 | 19075.0% | +| LUA | 9 | 1548 | 209.3 | 1539 | 17100.0% | +| PROLOG | 9 | 1540 | 124.2 | 1531 | 17011.1% | +| RUST | 9 | 1537 | 191.5 | 1528 | 16977.8% | +| SCHEME | 15 | 1574 | 168.0 | 1559 | 10393.3% | +| OBJC | 17 | 1559 | 168.4 | 1542 | 9070.6% | +| POWERSHELL | 14 | 1269 | 128.0 | 1255 | 8964.3% | +| PYTHON | 19 | 1574 | 174.8 | 1555 | 8184.2% | +| OCAML | 19 | 1537 | 168.4 | 1518 | 7989.5% | +| TCL | 20 | 1572 | 187.1 | 1552 | 7760.0% | --- @@ -666,6 +675,7 @@ Individual Reports → Aggregation Script → Chart Generation (via UN) → Fina - `reports/4.2.8/perf.json` - 645 tests, generated 2026-01-23T10:01:33Z - `reports/4.2.9/perf.json` - 645 tests, generated 2026-01-23T10:05:34Z - `reports/4.3.0/perf.json` - 820 tests, generated 2026-02-06T15:25:06Z +- `reports/4.3.1/perf.json` - 824 tests, generated 2026-02-08T11:44:58Z Each `perf.json` contains: @@ -862,5 +872,5 @@ For questions about this methodology or to report issues: --- **Generated by UN Inception Performance Analysis Pipeline** -**Analysis Date:** 2026-02-06T10:26:41.836993 +**Analysis Date:** 2026-02-08T06:46:35.375632 **Report Version:** 1.0.0