Pith Number

pith:IK4XATJ4

pith:2026:IK4XATJ4NGATPHCGKAX3HP5HLI

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A Feature-Driven Framework for Software Fault Prediction

Ahmad Nauman Ghazi, Ashir Javeed, Fahed Alkhabbas, Khalid AlKharabsheh, Nagajyothi Devarapalli, Sadi Alawadi

Combining correlation-based feature selection with genetic algorithm tuning reaches 88.4 percent accuracy for software fault prediction with random forest.

arxiv:2605.17611 v1 · 2026-05-17 · cs.SE · cs.LG

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Claims

C1strongest claim

The combined application of CFS and GA yielded the highest accuracy, achieving 88.40% with RF, representing an improvement of 18% over baseline models without feature selection or tuning.

C2weakest assumption

That the performance improvements generalize beyond the specific (unspecified) datasets and that the baseline models without feature selection or tuning provide a fair and representative comparison.

C3one line summary

Combining correlation-based feature selection with genetic algorithm tuning on random forest achieves 88.40% accuracy for software fault prediction, an 18% gain over baselines without selection or tuning.

References

35 extracted · 35 resolved · 0 Pith anchors

[1] Software defect association mining and defect correction effort prediction, 2006

[2] Experimental study on software fault prediction using machine learning model, 2019

[3] Fault prediction for large scale projects using deep learning techniques, 2022

[4] An empirical study of some software fault prediction techniques for the number of faults prediction, 2017

[5] A promethee based evaluation of software defect predictors, 2018

Formal links

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Receipt and verification

First computed	2026-05-20T00:04:48.442211Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

42b9704d3c6981379c46502fb3bfa75a37249310038b93e69c3ae8c1e732ecf2

Aliases

arxiv: 2605.17611 · arxiv_version: 2605.17611v1 · doi: 10.48550/arxiv.2605.17611 · pith_short_12: IK4XATJ4NGAT · pith_short_16: IK4XATJ4NGATPHCG · pith_short_8: IK4XATJ4

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/IK4XATJ4NGATPHCGKAX3HP5HLI \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 42b9704d3c6981379c46502fb3bfa75a37249310038b93e69c3ae8c1e732ecf2

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2026-05-17T19:16:20Z",
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