Which software engineering technologies are foundational? Fundamentals such as algorithmic thinking and diagnosing defects inform emerging skillsets in areas like cloud-native computing and DevSecOps.
Which trends are shaping software engineering’s future? Leading forces include AI-driven software engineering, built-in security, and distributed architectures.
What challenges does the field face? Grappling with data security and academic integrity in the AI age are among the major challenges.
What are promising career paths in software engineering? Perennial skills and new paths converge in roles ranging from QA engineers to automation architects.
Which ethical challenges are most urgent? Challenges here include building safe, reliable systems in an age of technology disruptions — and ensuring that those systems offer clear accountability when they falter.
How can I stay up-to-date on software engineering news and research? Access the latest software engineering standards, SME insights, and industry trends.

Learn more about the field at the International Conference on Software Engineering (ICSE)

Skills in Software Engineering Today

“Learn programming, data structures, algorithms, operating systems, databases, and networks. That is what lets you see what is going on under the code you write. Logic, probability, and statistics help when you need to ask: Is this correct, is it fast enough, and how sure are we?”–Ashly Joseph, CISCO

Following are examples of established and emerging software engineering knowledge and skill areas. As the article, “Skills That Shape Your Path Beyond Coding” points out, getting a grasp on everything from the Software Engineering Body of Knowledge (SWEBOK) to essential soft skills can ease your entry into the workplace.

What Knowledge Is Essential in Software Engineering Today?

As SWEBOK highlights, core competencies remain central to building high-quality software. Among those core competencies are the following:

Programming and algorithmic thinking. Designing efficient, scalable systems requires understanding data structures, algorithms, and computational complexity.
Problem-solving and computational thinking. Managing complexity requires examining problems from multiple angles, breaking them down into steps, and creating solutions at scale. Computational thinking focuses on patterns and edge cases, which offer diverse perspectives on the best approaches to problems.
Programming languages: A strong foundation in programming languages (such as Python, JavaScript, Java, or Go) remains essential; even when AI is writing the code, software engineers are expected to review, maintain, and secure it, making language understanding crucial.
Debugging and problem-solving. Systematically diagnosing and resolving software defects remains both routine and critical.
Cloud platforms and deployment. Understanding how cloud platforms work and how software is deployed at scale is essential to maintaining consistency across development and reliability after deployment.
Security and privacy. Ensuring basic security and privacy requires an understanding of secure code, authentication systems, password hashing, token-based authentication, and session-based authentication.

What New Skills Are Emerging in Software Engineering?

As the software engineering scope expands, organizations increasingly expect developers to combine traditional knowledge with skills in automation, infrastructure, and system reliability practices.

AI-augmented development. Working with AI coding assistants that generate code, documentation, and tests is increasingly common, and software engineers must be able to oversee that process and critically evaluate and review the resulting code.
Cloud-native development. Understanding containers, microservices, and cloud deployment pipelines is emerging as an essential skill for building scalable applications.
Integrating security into development (DevSecOps). Integrating security practices throughout the development lifecycle is becoming increasingly common, as are expectations that developers understand secure coding practices, automated security testing, and container/cloud security.
Observability and reliability engineering. Maintaining distributed systems requires skills in monitoring system behavior, analyzing telemetry, and responding to incidents.

What Are Key Trends in Software Engineering?

“AI agents are starting to run large parts of software work, not only suggest the next line. They can plan a change, edit many files, run tests, open a pull request, and in some teams help operate the system after it ships. That shortens the path from an idea to running software.” –Ashly Joseph, CISCO

AI advancements and the growing reliance on software across industries are driving major trends in software engineering, influencing how modern software systems are designed, built, and operated.

AI-Driven Software Engineering

According to Gartner, 90% of enterprise software engineers will use AI code assistants within two years. The result? A shift in their role from code creation, testing, and documentation to system design, orchestration, and oversight. As a result, skills in AI governance, ethical AI practices, and prompt engineering will be increasingly essential.

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Cloud-Native and Distributed Architectures

Cloud-native architectures, including microservices, serverless, and API-first system designs, offer scalability and resilience within globally distributed systems. For organizations, using modular pipelines and hybrid cloud environments enables faster releases and improved uptime service-level agreements.

This on-going trend creates opportunities for engineers with competencies in

Site reliability engineering (SRE)
DevOps
Infrastructure-as-code
Distributed system design

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Integrated DevSecOps and Built-In Security

To reduce costly breaches and meet regulatory demands, organizations will increasingly integrate security teams into their development and operations workflows.

In this DevSecOps framework, all three teams focus on embedding security into their daily workflows and taking shared ownership of security outcomes. To address this shift, engineers can focus on skills such as threat modeling and secure coding, as well as gain familiarity with automated vulnerability and compliance tools.

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What Are Key Software Engineering Challenges?

“We can build software fast. The hard part is still understanding it, securing it, and keeping it in good shape.” –Ashly Joseph, CISCO

In addition to general issues such as declining budgets, the software engineering field faces emerging challenges in two pivotal areas: use of AI and increasing system complexity. Both of these factors are rapidly expanding, making the challenges they create urgent and difficult to manage. Following are some of these key challenges.

Creating Reliable AI

Use of AI in software engineering promises clear benefits, not least through productivity boosts. What remains murky, however, is how organizations can ensure that this AI use also delivers on trust and reliability.

At minimal, organizations must invest in explainability frameworks, governance, and auditing processes. Engineers will find evaluation and critical thinking skills especially valuable as their responsibilities expand to include oversight of AI outputs, including interpreting, validating, and verifying them.

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Mitigating Security and Supply-Chain Vulnerabilities

Up to 90% of software today is composed of open-source components, external tools, and third-party libraries. Each of these external dependencies offers attackers an entry point to
- Steal data
- Inject malware
- Build backdoors
Increased software complexity makes tracking individual dependencies massively challenging. Yet the stakes, which include operational disruptions, major breaches, and reputation destruction, are clearly high.

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Addressing the Workforce Skills Gap

The knowledge and skills required of today’s software engineers is shifting at the same rapid pace as automation and AI-integration. To reskill their teams, organizations must invest in training, mentorship, and upskilling programs while simultaneously investing in these new technology solutions.For software engineering professionals, continuous and life-long learning can ease adaptation to evolving paradigms, tools, and processes; indeed, it is more important than ever.

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Building a Career in Software Engineering Today

According to the World Economic Forum’s most recent Future of Jobs report, technology roles such as software and application developers remain among the fastest-growing occupations. Driving this demand is ongoing digital transformation across industries. Following is an overview of some of the sector’s new and emerging roles.

Software Engineer

Focus: Designing, developing, and maintaining software systems, including proficiencies in areas such as algorithms, data structures, testing, and database management.
Key sectors: Across sectors, including technology, finance, healthcare, manufacturing, aerospace, and defense.
Job titles: Software engineer, software developer, programmer, software development engineer (SDE), software analyst, full-stack engineer, software engineering manager.
Requirements: Bachelor’s degree in computer science, software engineering, or related field + knowledge of programming languages, problem-solving skills, data structures, and algorithms.
Key credentials: Software Professional Certification—Level 1, Professional Software Engineering Master Certification, and certifications in areas of specialization.
Average salary range: USD$97,500–$158,800
Where to network: International Conference on Software Engineering (ICSE)
Related reading: IEEE Software

Quality Assurance Engineer

Focus: Validating application quality using skills in testing, review, and assessment, as well as creating automated tests, tools, and methods.
Key sectors: Across sectors, including technology, finance, healthcare, manufacturing, aerospace, and defense.
Job titles: QA engineer, quality engineer, quality assurance technician, QA analyst.
Requirements: Bachelor’s degree in computer science, engineering, or IT + proficiency in programming languages and test automation tools.
Key credentials: ISTQB Certified Tester (Foundation Level), Certified Software Testing Engineer (CSTE), Certified Software Quality Analyst (CSQA).
Salary range: USD$57,000–$120,500+
Where to network: International Conference on Software Engineering (ICSE) and International Conference on Software Testing, Verification and Validation (ICST)
Related research: IEEE Transactions on Dependable and Secure Computing

Back-End Engineer

Focus: Building and maintaining server-side technology, databases, and application logic that power user-facing software; knowledge areas include system architecture, API development, data security, and performance.
Key sectors: Technology/SaaS, healthcare, finance, manufacturing, media, aerospace, and defense.
Job titles: Back end engineer; back end developer; software engineer (back end); backend/API engineer; full-stack developer (back-end focus).
Requirements: Bachelor or master’s degree in computer science or related field + 1–3 years’ experience with backend technologies, programming languages, DevOps tools and processes, database management, and security best practices.
Key credentials: Certifications are typically language and cloud-platform specific; examples include AWS Certified Developer (Associate) and Oracle Certified Professional: Java SE.
Salary range: USD$149,000–$242,000+
Where to network: International Conference on Software Engineering (ICSE)
Related research: IEEE Transactions on Software Engineering (TSE)

DevOps Engineer

Focus: Combining software development and IT operations to quickly and reliably deliver solutions.
Sectors: Across sectors, including telecoms, computer systems design, consulting.
Job titles: Site reliability engineer (SRE), platform engineer, cloud engineer, infrastructure engineer, systems engineer.
Requirements: Bachelor’s degree in computer science, information technology, or engineering + 3–5 years’ experience in DevOps, SRE, or software development roles.
Key credentials: AWS Certified DevOps Engineer—Professional, Certified Kubernetes Administrator (CKA), Microsoft Certified: DevOps Engineer Expert.
Salary range: USD$107,500–153,000+
Where to network: International Conference on Software Engineering (ICSE)
Related research: IEEE Transactions on Software Engineering (TSE)

AI/Machine Learning Engineer

Focus: Designing, building, and deploying machine learning models and AI systems.
Sectors: Across sectors, including technology, finance, healthcare, retail, and manufacturing.
Job titles: Machine learning engineer, AI engineer, data scientist, and research scientist; specialized titles include natural language process (NLP) engineer and computer vision engineer.
Requirements: Bachelor or master’s degree in computer science, engineering, information systems, or related field + 3–5+ years’ experience in software engineering and AI development.
Key credentials: Platform-related certifications include Google Professional ML Engineer, AWS Certified Machine Learning—Specialty, and Microsoft Certified: Azure AI Engineer Associate.
Salary range: USD$84,000–$182,000+
Where to network: IEEE Conference on Artificial Intelligence
Related research: IEEE Transactions on Software Engineering (TSE) and IEEE Transactions on Artificial Intelligence

Data Engineer

Focus: Using database knowledge and cloud computing skills to build pipelines and systems to move, clean, and organize data so that it’s useable by other teams and applications.
Sectors: Across sectors, including technology, finance, e-commerce, healthcare, telecom, manufacturing, and government.
Job titles: Data engineer, data platform engineer, extract-transform-load (ETL) developer, big data engineer, data warehouse engineer, analytics engineer.
Requirements: Bachelor or master’s degree in computer science, engineering, information systems, or related field + proven experience in data engineering or software development.
Key credentials: Platform-specific certifications include Google Cloud: Professional Data Engineer and Microsoft Certified: Azure Data Engineer Associate.
Salary range: USD$98,000–145,500+
Where to network: International Conference on Software Engineering (ICSE)
Related research: IEEE Transactions on Software Engineering (TSE)

Automation Architect

Focus: Using software architecture knowledge and AI skills to design workflows that incorporate AI tools into existing systems.
Sectors: Across sectors, including technology, automotive, aerospace, finance, and manufacturing.
Job titles: Automation architect, senior automation architect, robotic process automation (RPA) architect, test automation architect, and process automation engineer.
Requirements: Bachelor or master’s degree in data analytics, computer science, software engineering, or information technology + 3–5 years’ experience in designing and implementing distributed systems, data management, automation, and edge computing.
Key credentials: UiPath Certified Professional–Automation Solution Architect Professional, AWS Certified DevOps Engineer–Professional, Microsoft Certified: Azure DevOps Engineer Expert (AZ-400).
Salary range: USD$124,000–180,500+
Where to network: International Conference on Software Architecture
Related research: IEEE Transactions on Software Engineering (TSE) and IEEE Transactions on Emerging Topics in Computing

Edge Computing Architect

Focus: Designing distributed IT infrastructure to process data near its source to enable instant decision-making.
Sectors: Across sectors, including technology, manufacturing, automotive, healthcare, and telecommunications.
Job titles: Edge computing architect, edge software architect, edge solutions architect, edge IoT architect, AI solutions engineering (edge focus).
Requirements: Bachelor or master’s degree in computer science, software engineering, or a related discipline + expertise in cloud/edge platforms, distributed systems, C++/Python programming, and networking.
Salary range: USD$46,000–98,500+
Where to network: IEEE International Conference on Edge Computing and Scalable Cloud
Related research: IEEE Transactions on Software Engineering (TSE) and IEEE Transactions on Emerging Topics in Computing

Ethical Issues in Software Engineering

To design, develop, and deploy responsible systems that are safe, ethical, and have clear accountability paths is a huge challenge today, but it is one that goes to the heart of software engineering ethics.

According to the IEEE Computer Society/ACM Code of Ethics for Software Engineering, software engineers must:

Prioritize the public interest, safety, and welfare over their own or their clients’ interests
Maintain professional independence and integrity

To do this amid massive disruptions in technology, the job market, and the wider political world require strong commitment to ethical principles in pivotal areas, including the following.

AI: Safety, Bias, and Privacy

The inaugural 2025 International AI Safety Report written by more than 100 global AI experts noted that while various mitigation techniques exist for established AI harms such as safety, bias, and privacy, so far, “no combination of techniques can fully resolve them.”

Ongoing and potentially catastrophic safety concerns include AI generation of

Erroneous code
Inaccurate information
Hallucinated facts

Also unresolved: Bias with respect to race, gender, culture, age, disability, and other aspects of human experience; and privacy violations, which can range from illegal data collection to mass data leaks to deepfakes.

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Environmental Sustainability

Sustainable software engineering is an emerging field, but the stakes are as high as the ozone layer and as deep as your local water table. As Accenture notes in its recent report, Powering Sustainable AI:

“The exponential rise of AI is having a dramatic and unsustainable impact on the planet’s energy use, carbon emissions, and water consumption.”

Sustainable software engineering focuses on minimizing resource usage in software development; some efforts couple this resource-focus with a broader definition of “environment” that encompasses factors such as digital equality and social inclusion.

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Transparency and Accountability

AI’s rapidly expanding use in critical system software in sectors ranging from healthcare to defense to financial systems raises key questions, not the least of which are

How are decisions being reached?
Who is responsible for a system’s output and behaviors?

The “how” relates to system transparency; that is, understanding how a system makes its decisions. The “who” identifies the organizations and individuals responsible for what a system does. In software engineering, researchers note that answering these questions requires “deliberate design choices, including explainability tools, logging mechanisms, and governance processes.”

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AI’s Impact on Human Expertise

Although it’s early days in AI world, research is already showing that AI dependencies can degrade human cognition. In software development, relying on automated tools could degrade the expertise of the very humans charged with overseeing and validating the AI outputs.

Trust is also an issue; because it can be hard to understand how AI generates a particular bit of code, validating its correctness (or security) can be challenging. In the face of such challenges, developers new to oversight might simply accept the AI-generated code, increasing the risks for security and system failures.

Learn more:

Resources: The Learning Technologies Knowledge Hub

Stay up-to-date with the latest on learning technologies by accessing our Tech News blog, which is updated daily with the insights, trends, and research related to all things computing. Among recent related articles are the following:

As SWEBOK highlights, core competencies remain central to building high-quality software. Among those core competencies are the following:

Programming and algorithmic thinking. Designing efficient, scalable systems requires understanding data structures, algorithms, and computational complexity.
Problem-solving and computational thinking. Managing complexity requires examining problems from multiple angles, breaking them down into steps, and creating solutions at scale. Computational thinking focuses on patterns and edge cases, which offer diverse perspectives on the best approaches to problems.
Programming languages: A strong foundation in programming languages (such as Python, JavaScript, Java, or Go) remains essential; even when AI is writing the code, software engineers are expected to review, maintain, and secure it, making language understanding crucial.
Debugging and problem-solving. Systematically diagnosing and resolving software defects remains both routine and critical.
Cloud platforms and deployment. Understanding how cloud platforms work and how software is deployed at scale is essential to maintaining consistency across development and reliability after deployment.
Security and privacy. Ensuring basic security and privacy requires an understanding of secure code, authentication systems, password hashing, token-based authentication, and session-based authentication.