Software development trends and predictions for 2025

As the world races towards 2025, Developer examines what lies ahead for software development in the new year.

Among the most pressing trends for 2025 are AI development simplification, the integration of cross-functional engineering teams, and the evolution of DevSecOps practices. These shifts promise to redefine how companies approach innovation, security, and efficiency in their development pipelines.

AI-powered development simplification
Avthar Sewrathan, AI Product Lead at Timescale, predicts a significant transformation in the tech stack landscape by 2025.

“The traditional tech stack will continue to collapse,” Sewrathan notes, highlighting advancements driven by APIs and tools that reduce complexity and enable faster development. This shift is anticipated to empower engineers of all skill levels to create impactful AI applications with ease.

“By 2025, the traditional tech stack will continue to collapse, fueled by APIs and tools that reduce complexity and enable faster development,” Sewrathan explains. “Companies that deliver frictionless, accessible solutions with low barriers to entry will empower engineers to create impactful AI apps, no matter their level of expertise.”

Developer role evolution
Indu Keri, General Manager and Head of Engineering for Hybrid Cloud at Nutanix, envisions a new era of software development starting in 2025.

“Software development and engineering is already being democratised with tools like Copilot,” Keri says.

By combining GenAI with low-code solutions, code will be abstracted to the point of irrelevance. AI will generate applications rather than code, enabling developers to step into strategic roles.

“The day-to-day for developers will change drastically,” Keri notes. “No longer hamstrung by grunt work, developers will step into an up-leveled, strategic role—communicating with decision-makers about how tech can solve business problems.”

Agentic AI and DevOps automation
The hype around agentic AI will reach boiling point in 2025 as organisations bundle specialised agents for different stages of software delivery, including code generation, testing, and quality assurance. This orchestrator-driven approach will drive a more intelligent approach to DevOps automation.

Martin Reynolds, Field CTO of Harness, says: “Instead of having one, basic GenAI assistant, organisations will bundle together specialised agents for the different stages of software delivery—such as code generation, testing, and quality assurance.”

Integration of cross-functional engineering teams
Moving beyond AI development, the prediction for cross-functional engineering teams is another area of significant change. Reynolds outlines a future where standalone DevOps teams are phased out in favour of more holistic groups that include representation from all engineering disciplines.

“In 2025, organisations will move away from standalone DevOps teams towards more holistic groups that include representation from all engineering disciplines,” Reynolds explains.

Evolution of DevSecOps practices
The software development landscape is also witnessing a significant evolution in DevSecOps practices.

Dylan Thomas, Senior Director of Product Engineering at OpenText Cybersecurity, predicts that, by 2025, DevSecOps will evolve beyond the “shift-left” paradigm to embrace a more mature “shift everywhere” approach.

This shift will require organisations to apply the right tools at the right stages of the DevSecOps cycle, improving efficiency and effectiveness in security practices.

Software Bill of Materials (SBOM) maturity

One of the critical components in this evolving landscape is the Software Bill of Materials (SBOM).

Government mandates are driving organisations to adopt SBOMs at scale, requiring them not only to generate these documents but also to make them actionable.

PostgreSQL’s role in managing complex data types
PostgreSQL’s extension capabilities are also anticipated to play a significant role in managing complex data types in one place.

“In 2025, PostgreSQL will solidify its position as the go-to ‘everything database’—the first to fully integrate AI functionality like embeddings directly within its core ecosystem,” says Avthar Sewrathan, AI Product Lead at Timescale.

“This will streamline data workflows, eliminate the need for external processing tools, and enable businesses to manage complex data types in one place.”

Diverse app stores will reshape mobile distribution
The app store ecosystem is poised for a significant shift as mobile carriers are expected to regain their influence over app distribution.

Matt Tubergen, EVP of Global Strategy & Partnerships at Digital Turbine, said: “The app store ecosystem is set to shift away from an Apple and Google duopoly to an open landscape as mobile carriers regain the power to connect consumers with apps.”

Emerging regulatory changes are also playing a critical role in leveling the playing field. These include bans that prevent tech giants like Google from incentivising app store preloads, which allow carriers and device manufacturers to preload their own apps on new devices.

Emergence of xOps
The convergence of DevOps, DataOps, and ModelOps into a new “xOps” paradigm is expected as more traditional applications adopt AI capabilities.

“This new-found set of dependencies will dramatically accelerate ‘AI-aware’ Release Orchestration while also challenging operations teams, support teams, QA teams, and more as the line between more traditional declarative applications blur with the new dependencies to LLMs and GenAI capabilities,” explains Derek Holt, CEO of Digital.ai.

Increasing security challenges
While AI-generated code can significantly increase developer efficiency, it also introduces security risks due to increased volume of code that must still be tested for vulnerabilities and errors.

“While research has shown that generative AI can halve the time it takes developers to complete coding tasks, the increased volume of code being produced must still be tested for vulnerabilities and errors,” warns Holt.

“This will increase security risk and lead to additional toil in the later stages of delivery, wiping out any efficiency gains from AI-generated code.”

Heightened regulatory scrutiny
Despite the increasing security challenges, a failure to secure software could have ever more serious repercussions. In light of increasing awareness and demand for reliable software systems, developers must prepare for heightened regulatory scrutiny.

Martin Reynolds, Field CTO of Harness, comments: “In the wake of the CrowdStrike outage, there will be an increase in regulatory pressure around poor-quality software.”

By adopting these strategies, organisations not only bolster software resilience but also enhance trust and brand loyalty among consumers. As regulations evolve to emphasise software reliability akin to cybersecurity standards, being proactive will be key to supporting business growth and staying ahead of regulatory measures. This approach not only mitigates risks but positions companies favorably in a landscape where quality is increasingly synonymous with competitive advantage.

Overall, the predictions for 2025 paint a picture of an industry bracing for radical transformation—driven by advancements in AI development, cross-functional engineering teams, and mature DevSecOps practices. These changes promise to accelerate innovation while enhancing security and user experience across industries.

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Can You Combine Design Thinking with DevOps in Software Engineering?

As we enter 2024, the dynamics of product management are shifting towards a blend of technological savvy, user-centric design thinking, and strategic agility. This article delves into the 15 essential product management skills that significantly affect how products are conceived, developed, and launched in today's market. These skills form the backbone of effective product management, from strategic thinking that aligns product development with long-term business goals to data-driven decision-making that ensures informed and impactful choices. Additionally, soft skills like leadership, communication, and stakeholder management have become indispensable in navigating the complex cross-functional landscapes typical of modern organizations.

Importance of Developing Strong Product Management Skills
Developing strong product management skills is crucial in today's fast-paced and competitive business environment. Product management encompasses a broad range of responsibilities that bridge the gap between the development team, stakeholders, and the market. The importance of these skills can be broken down into several key areas:

1-Understanding Market Needs
Customer Insight: Product managers with strong research and analytical skills can better understand customer needs and market demands.
Competitive Analysis: A deep understanding of the competitive landscape helps product managers position their products effectively, identify gaps in the market, and exploit opportunities for differentiation.
2-Strategic Vision and Decision Making
Product Strategy: Effective product management involves setting clear goals and developing a strategic vision for the product.
Prioritization: With often limited resources and time, the ability to prioritize tasks based on their potential impact on the market and alignment with business objectives is crucial.
3-Cross-Functional Leadership and Communication
Team Coordination: Product managers must work closely with diverse teams, including development, marketing, sales, and customer support.
Stakeholder Management: It is vital to manage expectations and communicate effectively with stakeholders, including investors, executives, and customers.
4-Execution and Problem-Solving
Agile Methodologies: Familiarity with agile development practices allows product managers to adapt to changes quickly, make iterative improvements based on feedback, and efficiently manage the product development process.
Risk Management: Identifying potential risks and obstacles early and planning to address them is crucial for minimizing disruptions to the product development timeline and ensuring the product's successful launch.
5-Customer-Centric Approach
User Experience (UX): A strong focus on UX ensures that the product is not only functional but also intuitive and enjoyable to use.
Feedback Loop: Establishing a continuous feedback loop with users helps make data-driven decisions, improve the product over time, and stay responsive to customer needs and market trends.

Qualifications Needed to Become a Product Manager
Becoming a product manager (PM) involves a blend of formal education, practical experience, and key skills that allow an individual to excel in managing products from conception to launch and beyond.

Formal Education
Bachelor’s Degree: Degrees in business, computer science, engineering, marketing, or related fields can provide a solid foundation.
Master’s Degree: An MBA or a master's degree in a relevant field can be advantageous, especially for advancement into higher management roles.
Practical Experience
Industry Knowledge: It is crucial to understand the industry in which you wish to work (e.g., tech, healthcare, finance). This can be gained through work experience, internships, or personal projects.
Cross-functional Experience: Experience in roles that interact with multiple facets of a business (such as sales, marketing, engineering, or customer service) can be incredibly valuable.
Product Management Experience: Direct experience as a product manager or in a product management-related role (e.g., product owner, project manager) can be essential.
Skills and Competencies
Technical Skills: While not always mandatory, technical skills or understanding of the technology relevant to the product can be extremely beneficial, especially in the tech industry.
Business Acumen: A strong grasp of business fundamentals, market analysis, and strategy is crucial for making decisions that align with the company's goals.
Communication and Leadership: Excellent verbal and written communication skills, along with leadership qualities, are essential for leading teams, negotiating with stakeholders, and advocating for your product.
Critical Thinking: The ability to analyze problems, think critically, and devise effective solutions is key in the role of a PM.
Customer-focused: Understanding customer needs and translating them into product features is fundamental. This often requires strong research and analytical skills.
Agility and Adaptability: The product landscape can change rapidly, so it is vital to be able to adapt and manage change.
Certifications and Continuous Learning
Product Management Certifications: Certifications such as Professional Certification In Product Management, Product Management Professional Program, or Scrum Product Owner certification can enhance a resume.
Continuous Learning: Engaging in continuous learning through workshops, seminars, online courses, and industry conferences can keep a PM's skills sharp and relevant.
Networking and Personal Branding
Networking within professional communities and building a personal brand can also be instrumental in launching and advancing a career in product management.

The original content of the note was published on Simplilearn.com. To read the full note visit here

Can You Combine Design Thinking with DevOps in Software Engineering?

Design thinking, known as a human-centered approach, involves solving complex problems by understanding users, defining their needs, ideating creative solutions, prototyping and testing them, and iterating the process until an optimal outcome is achieved.
On the other hand, DevOps is a methodology that intertwines software development and operations teams and paves the way for delivering software products and services with increased speed, efficiency, and cost-effectiveness.
What happens when the two meet? Can they be a force multiplier in software engineering? That is our question for the day.
Design Thinking and DevOps: Origins and Evolution
Both design thinking and DevOps have emerged as central methodologies, enhancing user-centricity, promoting agility, and driving innovation in software development and delivery.
DevOps traces its roots to software engineering, system administration, and quality assurance, expanding its influence across Web development, cloud computing, and data science.
Design thinking originates in design, engineering, and psychology, with applications spanning product development, service design, social innovation, and education.
Where they cross over as disciplines include user focus, collaboration, experimentation, feedback, and iteration.
How Design Thinking and DevOps Work Together
Integrating design thinking into DevOps necessitates a nuanced understanding of their respective steps and tools and an approach that employs the strengths of both methodologies.
The “empathize, define, ideate, prototype, and test” phases of design thinking align with DevOps’s “plan, code, build, and run” stages, forming a cycle that supplements the software development lifecycle.
Design thinking tools and techniques include interviews, surveys, observations, personas, journey maps, brainstorming, sketching, and usability testing, which guide the exploration of user needs and the creativity of creative solutions.
Simultaneously, DevOps tools and techniques, such as version control, code review, continuous integration, continuous delivery, configuration management, containerization, and cloud computing, create an integrated pipeline accommodating iterative design processes within a continuous delivery framework.
Moreover, integrating design thinking and DevOps extends beyond merely synchronizing processes and tools; it cultivates a collaborative mindset within cross-functional teams.
Best Practices and Tips
To ensure an effective implementation of design thinking and DevOps, the following best practices and tips can be a help:
-Align the goals and perspectives of the design, development, and operations teams to ensure a shared vision and understanding of user needs, the solution, and the value proposition.
-Involve users and stakeholders throughout the process, soliciting and incorporating their feedback and suggestions, validating assumptions and hypotheses, and adopting a culture of co-creation and co-innovation.
-Embrace a culture of experimentation and learning from failures. Test ideas and prototypes early and often, iterate, and improve solutions based on feedback and data.
-Automate and optimize the process, employing appropriate tools and techniques to enhance the software delivery lifecycle. Reduce manual and repetitive tasks to increase the speed and quality of the software product or service.
Challenges and Solutions
The integration of design thinking and DevOps brings some challenges as well. However, these challenges can be effectively addressed.
Resistance to change is challenging as team members, accustomed to traditional methodologies, show reluctance and skepticism toward integrating design thinking and DevOps. Proactively communicating benefits, comprehensive training, and celebrating successes are essential to address this.
The diverse skillset requirements of design thinking and DevOps present another challenge. Integrating individuals with varying expertise and backgrounds can lead to disparities in understanding and collaboration.
The Bottom Line
The convergence of design thinking and DevOps can be a valuable tool in the right company, creating an integrated approach that can result in impactful outcomes, increased user satisfaction, expedited time-to-market, and better collaboration.
It may need experts and advocates in both disciplines, and challenges can include resistance to change, but when it works well, you open the door to creativity and innovation, and you can really unlock the power of your workforce.

The original content of the note was published on Techopedia.com. To read the full note visit here