Research questions in software assurance and digital quality
This section organizes technical questions, educational methods, and research directions. Work is identified by its actual status. A practitioner article explains an argument. A proposed study describes a method to be carried out. A completed study requires reported methods, results, limitations, and accessible supporting material where sharing is permitted.
The educational examples on CSVtoCSA are not evidence of measured project outcomes. They help make methods understandable and testable.
Research direction 1: Evaluating AI-assisted validation-document review
Status: Proposed study. No empirical results are claimed on this page.
Question
Under what conditions does an AI-assisted workflow help qualified reviewers identify protocol issues without increasing consequential missed findings or unsupported conclusions?
Proposed method
Create a documented collection of fictional or appropriately authorized protocols and requirements. Establish reference findings through independent qualified review and adjudication. Separate development cases from evaluation cases. Define the AI configuration and permitted use before evaluation.
Compare assisted and unassisted review using a study design that addresses differences between reviewers, case difficulty, assignment order, and learning. Measure final reviewer findings, severity-specific misses, false findings, source-verification behavior, and total review effort.
What would be reported
Describe the dataset, inclusion criteria, reference process, configuration, matching rules, denominators, comparison design, results, uncertainty, and limitations. Report unfavorable outcomes and failed cases. Share synthetic materials and analysis instructions where appropriate so others can examine the work.
Boundaries
Results from one document type or reviewer group should not be generalized automatically. Synthetic documents may not represent the complexity of real projects. Confidential project materials require appropriate authorization and cannot be made public merely to strengthen a portfolio.
Research direction 2: Decision consistency in SaaS change assessment
Status: Proposed study.
Question
Does a structured change-impact worksheet help practitioners make more consistent and better-explained assurance decisions than an unstructured release-note review?
Proposed method
Prepare fictional SaaS releases with different effects: cosmetic display, conditional routing, shared date logic, signature export, API behavior, and incomplete supplier information. Have practitioners assess affected uses, evidence gaps, proposed activities, and release conditions.
Evaluate whether reasoning identifies the intended dependencies and failure paths. Use independent adjudication and an explicit rubric rather than assuming one author’s answer is uniquely correct. Measure agreement and explanation quality separately from effort.
Expected contribution
A transparent case set, scoring rubric, analysis, and discussion of disagreement could help practitioners see which questions improve assessment. This is a proposed contribution, not a claim that the method has already demonstrated improvement.
Research direction 3: What makes assurance evidence reviewable?
Status: Proposed study.
Question
Can another qualified reviewer reconstruct the basis for a release decision from the evidence package without informal explanation?
Proposed method
Create comparable fictional evidence packages that differ in traceability, context, exception handling, and presentation. Ask reviewers to identify the use, tested failures, unresolved issues, and decision limits. Record accuracy, ambiguity, time, and requests for missing information.
Practical value
The study could inform better evidence records and training examples. It should not assume that shorter records are always better or that more documentation necessarily improves understanding.
How research is distinguished from commentary
A research report needs a defined question and reproducible method, not just a technical title. A named method needs clear attribution to its foundations and evidence before claims of novelty or effectiveness. External publication and peer-review status should be described exactly as they occurred.
For self-published writing, use the label “self-published practitioner article.” For a conference presentation, identify the actual event, date, role, and available proceedings. For a completed study, link the report, methods, and permitted supporting materials.
Filter the publication and research record
External publication record
Validating the Future series
Five self-published practitioner articles on Sandip’s public LinkedIn profile. These records are not represented as peer reviewed.
Practitioner article · Self-published on LinkedIn · Series part 1
Validating the Future — Part 1: From Simulation to Reality: Why AI Systems Need Continuous Validation
Explains why pre-release evidence is only the starting point for AI assurance and why post-deployment performance, drift, and context changes require a continuous validation loop.
- Author
- Sandip Thorat
- Publisher / date
- LinkedIn · 2026
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- continuous validation, AI assurance, model monitoring, regulated life sciences
- Author contribution
- Conceptualization, practitioner analysis, writing, and publication.
- Suggested citation
- Thorat, S. (2026). “Validating the Future — Part 1: From Simulation to Reality: Why AI Systems Need Continuous Validation.” LinkedIn.
Practitioner article · Self-published on LinkedIn · Series part 2
Validating the Future — Part 2: The Evolution of Validation for Intelligent Systems
Examines how validation planning, testing, change control, and monitoring apply when system behavior can change with data, configuration, and operating context.
- Author
- Sandip Thorat
- Publisher / date
- LinkedIn · 2026
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- intelligent systems, validation lifecycle, changing software, validation approach
- Author contribution
- Conceptualization, practitioner analysis, writing, and publication.
- Suggested citation
- Thorat, S. (2026). “Validating the Future — Part 2: The Evolution of Validation for Intelligent Systems.” LinkedIn.
Practitioner article · Self-published on LinkedIn · Series part 3
Validating the Future — Part 3: How Generative AI Is Beginning to Transform Testing Itself
Discusses how generative AI changes test design, scenario generation, and review of test results while preserving accountable human judgment and challenge.
- Author
- Sandip Thorat
- Publisher / date
- LinkedIn · 2026
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- generative AI, test design, validation records, human oversight
- Author contribution
- Conceptualization, practitioner analysis, writing, and publication.
- Suggested citation
- Thorat, S. (2026). “Validating the Future — Part 3: How Generative AI Is Beginning to Transform Testing Itself.” LinkedIn.
Practitioner article · Self-published on LinkedIn · Series part 4
Validating the Future — Part 4: Continuous Validation & Autonomous Quality Systems
Connects monitoring, feedback, testing, and governance into an operating model for quality systems that increasingly make or recommend decisions autonomously.
- Author
- Sandip Thorat
- Publisher / date
- LinkedIn · 2026
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- continuous validation, autonomous quality systems, monitoring, governance
- Author contribution
- Conceptualization, practitioner analysis, writing, and publication.
- Suggested citation
- Thorat, S. (2026). “Validating the Future — Part 4: Continuous Validation & Autonomous Quality Systems.” LinkedIn.
Practitioner article · Self-published on LinkedIn · Series part 5
Validating the Future — Part 5: Algorithmovigilance: How to Monitor and Control AI Systems in the Real World
Frames algorithmovigilance as a practical post-deployment discipline for detecting drift, anomalous behavior, control degradation, and conditions requiring human intervention.
- Author
- Sandip Thorat
- Publisher / date
- LinkedIn · 2026
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- algorithmovigilance, model drift, post-deployment monitoring, AI controls
- Author contribution
- Conceptualization, practitioner analysis, writing, and publication.
- Suggested citation
- Thorat, S. (2026). “Validating the Future — Part 5: Algorithmovigilance: How to Monitor and Control AI Systems in the Real World.” LinkedIn.
CSVtoCSA
Practitioner articles
Practitioner article · Self-published
The AI Assurance Release Decision: From Model Scores to a Controlled GxP Use
How to combine AI performance, controls, human review and monitoring into a decision about a defined use.
- Author
- Sandip Thorat
- Publisher / date
- CSVtoCSA · 2026-09-05
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- AI Assurance, computerized systems assurance, regulated life sciences
Practitioner article · Self-published
The SaaS Version Problem: Assuring Software You Cannot Freeze
How to assess a supplier release, identify affected functions and choose the evidence needed for continued use.
- Author
- Sandip Thorat
- Publisher / date
- CSVtoCSA · Date not recorded
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- Supplier & SaaS, computerized systems assurance, regulated life sciences
Practitioner article · Self-published
Why “The Vendor Validated It” Is Not an Assurance Conclusion
How to decide which supplier evidence supports your configuration and which questions still need local evidence.
- Author
- Sandip Thorat
- Publisher / date
- CSVtoCSA · Date not recorded
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- Supplier & SaaS, computerized systems assurance, regulated life sciences
Practitioner article · Self-published
When Exploratory Testing Produces Better Evidence Than a Script
When exploratory testing can reveal important failures, and how to keep its findings reviewable.
- Author
- Sandip Thorat
- Publisher / date
- CSVtoCSA · Date not recorded
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- Testing, computerized systems assurance, regulated life sciences
Practitioner article · Self-published
Fewer test scripts do not automatically mean better assurance
How to judge a testing change by risk coverage and evidence quality instead of script counts.
- Author
- CSVtoCSA editorial
- Publisher / date
- CSVtoCSA · 2026-09-12
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- Testing, computerized systems assurance, regulated life sciences
Practitioner article · Self-published
A screenshot is evidence only when it answers a question
How to choose evidence that connects a test claim, the conditions and the observed result.
- Author
- CSVtoCSA editorial
- Publisher / date
- CSVtoCSA · 2026-09-12
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- Testing, computerized systems assurance, regulated life sciences
Practitioner article · Self-published
An AI reviewer can save time and still weaken a review
How to evaluate an AI-assisted review using missed issues, false findings and the final human decision.
- Author
- CSVtoCSA editorial
- Publisher / date
- CSVtoCSA · 2026-09-12
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- AI Assurance, computerized systems assurance, regulated life sciences
Practitioner article · Self-published
Supplier evidence does not remove local accountability
How to reuse supplier evidence while retaining responsibility for local configuration, records and changes.
- Author
- CSVtoCSA editorial
- Publisher / date
- CSVtoCSA · 2026-09-12
- Publication status
- Public practitioner article; not peer reviewed
- Keywords
- Supplier & SaaS, computerized systems assurance, regulated life sciences