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Optimizing Software Bug Prediction with Explainable AI: ASHAP-Based Analysis Using Random Forests

Students & Supervisors

Student Authors
Nura Solahin Esha
Master of Science in Computer Science, FST
Md. Owafeeuzzaman Patwary
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Firoz Ahmed
Professor, Faculty, FST
Tonny Shekha Kar
Lecturer, Faculty, FST

Abstract

Softwarebugpredictionmodelshelpoptimize testingresourcesandreducede velopmentcosts.Traditionalbugdetectionmethodsrelyonpredefinedrulesor machinelearningmodels,whichoftenlacktransparency,makingdebuggingdifficult. Whilemachinelearningmodelsachievehighaccuracy, theirblack-boxnaturelimits interoperability,makingitchallengingfordeveloperstounderstandthepredictions. Toaddressthis issue, thisstudyproposestheuseof ((XAI)withShapleyAddi tiveexPlanations(SHAP)toenhancebugdetectionbyprovidingclear insights into modelpredictions.SHAPispreferredoverLIMEbecauseof itsconsistentandaccu ratefeatureattribution,makingitmorereliablefordebugging.Additionally,XAI is chosenovertraditionalrule-basedordeeplearning(CNN)modelsbecauseitimproves transparency,allowingdeveloperstoquicklyidentifyandresolvesoftwaredefects.In thisstudy,weusedSHAPforinteroperabilityandtheNASAPROMISEdataset.The experimentalresultsshowthatourSHAP-basedmodelachieved85%accuracy.This approachaims tobridge thegapbetweenAI-drivenbugdetectionanddeveloper friendlyinterpretability,ensuringmoreefficientandtrustworthydebugging

Keywords

SHAP Random Forest

Publication Details

  • Type of Publication:
  • Conference Name: 3rd International Conference on Big Data, IoT and Machine Learning (BIM 2025)
  • Date of Conference: 25/09/2025 - 25/09/2025
  • Venue: Dhaka International University, Bangladesh