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PROJECT 07  ·  Research · Responsible AI

Responsible AI for Software Decisions
Making model behavior easier to inspect

A four-phase responsible-AI framework for software engineering decision support, evaluated on NASA’s JM1 defect-prediction data with SHAP-based explanations.

FIELD NOTES / 07—07
07
EXPLAIN
Bhanu Teja Malineni07 / 07
RoleCo-developer · Research
PeriodResearch project
ContextUnpublished research
FocusResearch

The idea

This research explored how transparent machine-learning systems could support software engineering decisions. The work paired a defect-prediction model with a structured responsible-AI framework and explanations intended to improve user understanding.

Approach

We co-developed a four-phase framework and evaluated it using NASA JM1 defect-prediction data. SHAP-based explanations were included to help users understand model outputs. The source material reports approximately 82% AUC and a 15–20% improvement in user comprehension.

01FrameworkCo-develop a four-phase responsible-AI approach for decision support.
02EvaluateUse NASA JM1 software defect-prediction data.
03ExplainApply SHAP explanations and report user-comprehension findings.

Reported outcomes

~82% AUCReported model result on NASA JM1.
15–20%Reported improvement in user comprehension with SHAP explanations.
4 phasesResponsible-AI framework developed collaboratively.
UnpublishedResearch status as documented in the source material.
This work is unpublished. AUC and comprehension figures are reported in the source resume and are not independently audited here.

Tools & methods

Responsible AINASA JM1Defect predictionSHAPExplainable AI
More about the project

The project focused on responsible model use in software engineering decision support. It combines model evaluation with explanation and reported user-comprehension outcomes.

What I took from it

Responsible-AI work needs to address how people interpret system outputs as well as how a model performs on a dataset.