Amit Puri
Studying Online Global Doctor of Business Administration (GDBA)ssbm.ch
Title
The impact on software quality due to numerous digital transformation efforts as a part of modernization or cloud adoption programs and the rise of low code/no code platforms viz Custom development
Aim
- Study factors on adoption of low code/no code platform viz. custom development
- the impact of Digital Transformation in modernization or cloud adoption projects on code quality,
- and how using Machine learning methods help solve technical debts problem.
Datasets
- https://github.com/src-d/datasets
- https://paperswithcode.com/dataset/public-git-archive
- https://ml4code.github.io/papers.html
- https://github.com/huggingface/datasets
Datasets Search
- ourworldindata.org
- datasetsearch.research.google.com
- datahub.io
- archive.ics.uci.edu
- kaggle.com
- https://github.com/odeke-em/drive
Research paper search
ABDC LIST 2019.pdf on iimj.ac.in
ABSTRACT
Background
The code quality of software applications is usually affected during any new or existing features development, or in various redesign/refactoring efforts to adapt to a new design or counter technical debts. At the same time, the rapid adoption of Microservices-based architecture in the influence of cognitive bias towards its predecessor Services-oriented architecture in any brownfield project could affect the code quality. Even a thoughtful attempt in the paradigm shift to microservices is iterative, massive investment, but code rework in this process too attracts technical debts. Technical debt is inevitable; controlling and reducing the impact is the only alternative. Moreover, owing to several factors
- challenging deadlines, lead time to market, cost constraints,
- ignoring warnings, bugs, and code smell from the static code analysis tools,
- the porting code to a more recent version of programming language or framework adds to more code smells, anti-patterns,
- and security vulnerabilities in an older codebase,
Digital transformation-related factors affecting software quality (WIP)
- organization not ready for digital transformation
- digital strategy is not in place
- strategy implementation failed due to poor execution
- lack of new capabilities to lead digital transformation
- effective change model
few organizations are resorting to Low-code/No-code (LCNC) platforms as SaaS to avoid this churn. No-code/low-code platform providers are leveraging microservices and serverless architecture for building their platforms and products. This study attempts to cover both the aspects of code smells in monolith and microservices architectural styles and factors influencing the decision-making of LCNC platform adoption.
Methods
A systematic study of code smells from the public datasets acquired from other research work on the monolith codebases and prepare a dataset for Microservices projects with static code analysis tools to get code smells. There are artefacts created in Microservice architecture for Infrastructure as Code (IaC) for CI/CD pipelines and Containerization. These artefacts are also a candidate for code smells, as researched by few. This study factor in Dockerfile, YAML, and other IaC artefacts for code smells. Perform exploratory data analysis of code metrics data from research work in monolith software and data collection from microservices-based code repositories to generate code metrics. These code metrics would undergo systematic data analysis, feature engineering, and machine learning model evaluation to study the patterns, the significance of code metrics, and factors leading to no-code/low-code platforms to provide recommendations over microservices/monoliths.
Initial Findings
Data class, large class and long method are no more significant code smell found in microservices than monoliths, while unnecessary/unutilised abstraction and long statement continue to remain as significant contributors to code smell in microservices. The magic number code smell remains indifferent in monolith and microservices codebases. Deficient encapsulation, cyclic-dependent modularisation and complex method and broken hierarchy are significantly less or none in microservices.
Further academic work
As a part of Doctorate program, study this topic in the context of bigger and more relevant dataset, or create a new one, and study further implications of software maintainability costs vs LCNC platforms, citizen development over custom software development.
References - to be considered in Literature review
- How to Alleviate Digital Transformation Debt: post-COVID-19 by Dr Setrag Khoshafian
- Why Digital Transformations Fail: The Surprising Disciplines of How to Take Off and Stay Ahead by Tony Saldanha
- Digital Transformation Debt: Part I/II
- How to Alleviate Digital Transformation Debt (excerpts and outline)
- Will Low-Code and No-Code Development Replace Traditional Coding?
- Software Quality: How Much Does It Matter?
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- https://github.com/features/copilot
- https://arxiv.org/abs/2202.13169
- https://github.com/VHellendoorn/Code-LMs
- Research: quantifying GitHub Copilot’s impact on developer productivity and happiness
- https://paperswithcode.com/paper/the-prevalence-of-code-smells-in-machine
- https://bvobart.github.io/mllint/
- https://cs.paperswithcode.com/paper/on-the-feasibility-of-transfer-learning-code
- https://towardsdatascience.com/should-you-use-a-no-code-ai-platform-limits-and-opportunities-4f39a92234f0
- https://towardsdatascience.com/no-code-low-code-ai-new-business-models-and-future-of-data-scientists-a536beb8d9e3
- https://www.g2.com/articles/low-code-and-no-code-machine-learning-platforms
- https://levity.ai/blog/no-code-ai-map
- https://analyticsindiamag.com/emerging-trends-in-low-code-no-code-platforms-in-ai/
- https://towardsdatascience.com/low-code-ai-in-enterprise-benefits-and-use-cases-b9692ee13168
- https://datascience.foundation/datatalk/no-code-machine-learning
- https://www.valoremreply.com/post/democratizationofai/
- https://serokell.io/blog/top-no-code-platforms
- https://indiaai.gov.in/article/decoding-the-what-why-behind-low-no-code-ai-does-it-matter-in-2022-beyond
- https://www.codesquire.ai
- https://data-goblins.com/power-bi/handover
- https://blog.kintone.com/business-with-heart/how-no-code-platforms-are-filling-the-developer-gap
- https://jukkaniiranen.com/2022/07/podcast-understanding-power-platforms-evolution/
- https://www.gartner.com/en/articles/10-automation-mistakes-to-avoid
- top-10-low-code-platforms-that-will-gain-prominence-in-2023
- four-causes-of-technical-debt-in-devops
- https://www.swissre.com/risk-knowledge/advancing-societal-benefits-digitalisation/remedy-risks-digitalisation.html
- The impact of culture on code
- https://hbr.org/2022/07/coding-isnt-a-necessary-leadership-skill-but-digital-literacy-is
- Factors influencing LCNC platform adoption by Matt, Citizen Development pioneer
- How to Alleviate Digital Transformation Debt: post-COVID-19 by Dr. Setrag Khoshafian
- This podcast by AgilePoint brings you fresh ideas from the trenches every week. It’s not a step-by-step guide but a sneak peek into the minds of experts with field-tested ideas, templates, and frameworks they have used to implement digital transformation programs, for other enterprises. If you want to be sure you get practical and focused advice about enterprise digital transformation in under an hour, don’t miss an episode.
- Sustaining Citizen Development beyond the Hype
- https://www.linkedin.com/posts/jeffreyrwinter_digitaltransformation-industry40-smartmanufacturing-activity-6977671529702510592-8too
MS Thesis link
orcid
https://orcid.org/0000-0002-6576-2984
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