A Retrospective
Study of Software
Analytics Projects:
In-Depth Interviews
with Practitioners

Prediction model (Resource Planning in Software Development)

The first four steps of software analytics
by Dongmei Zhang [3]

Step1 :Task definition

Step2 : data preparation

Step3 : analytic technology development

Step 4 : deployment
and feedback gathering

Defect detection effectiveness
Measure the value
added to the company
(reduction in defect rates
and testing effort) [4]

Some project ended without technology transfer [5]

Some project are ongoing [6]

Motivation

Explorer expected of prediction models

Understand how to implement prototype model to policy

Sample Data : 3 industrial case studies at a different stage - 12 practitioners

Defect prediction project : 5 develops (TelCo)

Interview Protocol

Quality prediction model : 1 Assurance Expert (TechCo)

Effort estimation model : 6 practitioners (BankCo)

Pre-interview [7] Guideline : Conducting In-Depth Interviews: A Guide for Designing and Conducting In-Depth Interviews for Evaluation Input: Monitoring and Evaluation

Pre-interview [8] Guideline : Designing Surveys: A
Guide to Decisions and Procedures,

Using four themes 3-5 questions each : Questions covered >>

Do software analytics projects ended successfully?

How much effort they required?

What is the main issues encountered
when building and deploying the technology?

What is practitioners’ expectations about
the output of these models in terms of
information content and performance?

What is the impact of these projects in terms of permanent changes throughout development
activities?

[9] “AI-Based Software Defect
Predictors: Applications and Benefits in a Case
Study,”

[11] Dione: An Integrated
Measurement and Defect Prediction Solution