AI in software engineering can boost productivity but also introduces serious risks—security flaws, data leakage, biased or hallucinated code, and regulatory exposure—so companies in Yogyakarta and beyond must adopt formal, continuous AI risk assessment frameworks before wide deployment. This article will discuss AI in software engineering challenge
Overview of the threat landscape
Unpredictable outputs and hallucinations: Generative tools can produce plausible-looking code that contains logic errors or security vulnerabilities.
Data leakage and IP exposure: Using third‑party AI assistants can inadvertently expose proprietary code or sensitive data to external models.
Regulatory and compliance risk: Emerging laws and standards (e.g., EU AI Act, OECD guidelines) require documented risk management and governance for AI systems.
Quick decision guide for engineering leaders
Key considerations: scope of AI use, data sensitivity, model provenance, monitoring capability, vendor risk.
Clarifying questions to answer now: Which teams use AI tools? What data do they feed into models? Are models open‑source, vendor‑hosted, or in‑house? What audit trails exist?
Decision points: Approve low‑risk uses (e.g., code formatting) quickly; require risk assessment for any AI touching production, PII, or IP.
Implementation roadmap (practical steps)
Inventory all AI tools and use cases across engineering teams; classify by risk level (development-only, production-affecting, PII/regulated).
Perform AI risk assessments for medium/high-risk systems: evaluate data lineage, model training data, explainability, and failure modes.
Enforce engineering controls: code review gates, SAST/DAST on AI outputs, access controls, and data minimization.
Governance and documentation: maintain audit trails, vendor due diligence, and compliance evidence aligned with international standards.
Continuous monitoring: instrument models and pipelines for drift, performance, and security alerts.
Building software is the coolest thing for software engineer. However, the software project will become a disaster when you ignore these seven sins that make disasters happen.
#1 Ignoring intensive communication
You can't depend on an email, quick chat, or might be a letter. Intensive communication is the basic ingredient of successful project
Pro Tips:
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Communicate regularly (weekly)
Update your progress regularly (monthly)
Create a IM group to handle urgent communication
#2 Undocumented Changes
Every time your customer said anything, record it. Every time your customer request changes, record it and negotiate
Pro Tips:
Avoid impulse response when changes are requested
Stored the changes and label it
Not all changes should be fulfilled
#3 Forgot to test
Your developer busy, your tester busy, the software should be deployed so you forgot the testing.
Pro tips:
Invest with automate test
Test with your customer
Integration testing is a must
#4 Virtual Documentation
You don't have user manual. You think that the software is easy enough to use.
Pro tips:
Creating short and quick manual
You can create video tutorial to make it clear
Write your manual when doing acceptance testing
#5 Yes, Sir!
Not all requested should be granted, not all changes should be fulfilled
Pro Tips:
Never say yes, until you discuss with your team
You should consider trade off for any changes
Prioritizing the changes
#6 Never Create a Plan
You feel the tasks are easy but try to measure it. You will find more complexity in the detail
Pro Tips:
Learning how to create break down structure, user story / tasks, or even Azure boars
Learning resource worksheet
Playing around with Microsoft Project will help
#7 Ignoring Payment Terms
You cannot build a software without money in your pocket. Therefore, you should configure the payment terms for each project.
Split the payment: beginning, middle, and end.
Milestone model will be helpful. You will get the payment every time you finish the job
Save the backup fund for supporting changes.
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On this video, we will discuss how to create and to avoid mislead pitching deck. We discuss seven tips that you need to avoid and to do when creating slide decks.
Find the useful link here
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This bahasa presentation shows the contextual of awareness in mobile application through avatar model. The Indonesian paper can be downloaded here, while the presentation is ahown below:
Teaching and sharing software engineering is hard, applying in the real world project is harder. If you are IT student or computer science you might be know that the software engineering is a boring stuff. Theoretical, Fiction example, and sometime it can be neglected. Many people love to do software engineering as a development activity. At least, some new people think software engineering can be done through great software and great coding skill. Yes it is!, Software development (coding-testing-deploy for short) can solve a lot of many software engineering aspect. However, we still need software engineering to create a foundation of a good software. If you are students, I propose you to learn basic software engineering such as. Season 1 - Software engineering fundamental introduction software engineering software process model software requirements software estimation software design software development software testing software configuration management software deployment and maintenance project management If you fall in love with software engineering you can learn advance aspect such as Season 2 - software engineering practices software methods Rational unified process Extreme Programming fundamentals Scrum fundamentals Lean fundamentals test driven development capability maturity model integration humans aspect software engineering software engineering certification Software engineering tools.