If somebody asks me whether generative ai services is a boon or bane for custom software development services, my impulsive response would be that it’s certainly not a bane for software development. But determining if the effect of Gen AI is net-positive for software development calls for a more nuanced discussion. The blog aims to qualify the impact of Gen AI on custom software development services, assess the positives and negatives, and determine the way forward for custom software developers.
Gen AI Accelerates Coding but Raises Questions on Code Stability
GitHub’s research shows that Gen AI assistants such as Amazon Q, GITHUB CoPilot, and ChatGPT have helped push more codes, while enabling developers to write codes 55% faster. In a blog post, Thomas Dohmke, Github CEO, paints a rosy picture1, emphasizing how these tools are redefining the custom software development experience. He notes that 92% of developers use AI both inside and outside of work and 76% of them feel more fulfilled at work.
However, Gitclear’s2 research tells a slightly different story. The software intelligence platform analyzed 153 millions of code authored between Jan 2020 and Dec 2023. While it acknowledges a considerable increase in added code, it also notes a proportional increase in churned code. Churned code refers to code that is reverted or updated within two weeks of being written.
“The main challenge with AI-assisted programming is that it becomes so easy to generate a lot of code which shouldn’t have been written in the first place.” – Adam Tornhill – code researcher
“The main challenge with AI-assisted programming is that it becomes so easy to generate a lot of code which shouldn’t have been written in the first place,” says Adam Tornhill, an acclaimed code researcher. Does it mean AI-assistant programming has an inverse effect on code quality? A study by Carnegie Mellon supports this notion, revealing that AI-generated codes introduce 35% more technical debt compared to human-written codes. Echoing the Gitclear research, it observes a significant increase in copy-pasted or duplicated codes and a corresponding decrease in moved or reuse codes
To put it in perspective, copy-pasted codes create a certain redundancy in the code base. The duplicated codes may seem like a quick solution to a junior developer who might merely copy and paste them instead of abstracting the codes into a reusable function or class or module. This subsequently leads to long-term problems in code maintainability and scalability.
On the other hand, moved codes centralize the logic, ensuring that future changes need to be made in a single place and not be repeated in every instance, unlike in copy-paste scenarios. This approach makes the software less prone to errors, and easier to maintain. With the proliferation of AI-generated codes, there is an increasing pressure on code maintainability, a factor that experienced developers recognize as critical, as poorly maintainable code can become costly over time.
With the proliferation of AI-generated codes, there is an increasing pressure on code maintainability, a factor that experienced developers recognize as critical, as poorly maintainable code can become costly over time.
Productivity Pressures May Dilute Engineering Ingenuity
Gen AI has been primarily positioned as a productivity acceleration tool rather than a creativity enhancer. However, the truth is, it serves as both – a productivity and creativity booster. But here’s my concern: by largely perceiving Gen AI as a tool for instant results, do we risk undermining the originality inherent in human input? In other words, as we increasingly embrace AI outputs, could we lose the human element that has always been indispensable to custom software development services?
As a matter of fact, custom software development services have historically provided novel solutions to otherwise unsolved or unique problems. At the center of these solutions, lie the ingenuity and original thinking of individuals. By focusing heavily on code acceleration, will the engineering teams be pressured to accept suboptimal AI-generated outputs and not arrive with original solutions?
Code Reviews shifting from architecture discussions to debating AI code suggestions
According to a study published in the journal of software engineering, junior developers who relied heavily on AI assistants showed a 40% decrease in problem solving capabilities when faced with novel challenges. It is not just the quality of individual inputs that is at stake, but also the effectiveness of team collaboration, which has taken a hit with the growing infusion of Gen AI. As a senior developer friend puts it, “In our code review meetings, we used to focus on architecture and design patterns. Now, those discussions have shifted toward reviewing AI-generated suggestions.”
“Will the developer become a code reviewer in the long run?”
That was a key question that emerged during the gathering of Trigent engineering teams. But the experts were quick to point out that whether a developer becomes a reviewer or retains the status of a coder ultimately depends on how they choose to engage with Gen AI
Developers can use Gen AI to generate instant drafts of code, which are often sub-optimal and require iterative prompting and fine-tuning. This means the developers need to invest time and effort to iteratively refine the results.
Or,
The developers may use Gen AI as a learning tool to accelerate their grasp of languages and complex codebases critical to custom software development services. They can also use Gen AI predominantly as an assistant, akin to a junior pair programmer, to complete less-complicated coding tasks, and boost efficiency.
Whether developers take the AI-generated codes at face value or refine them iteratively, it is important for future code readers (maintainers) to clearly distinguish between AI-generated and human-written code.
Ensuring Transparency: Distinguishing AI-Generated and Human-Written Code
“Be explicit with Gen AI. Let the readers know where and how generative AI technology is used in your codes.”
The advice from Steve Jones3, a tech blogger, stands tall. To expand on this, we must revisit our previous point about how AI-generated codes could complicate maintainability in the future. In this regard, Steve Jones advises to clearly mark those areas where Gen AI has been used. He illustrates 3 scenarios to drive home his point.
“Be explicit with Gen AI. Let the readers know where and how generative AI is used in your codes” – Steve Jones, a tech blogger
Scenario 1 : The codes are AI-generated
If the codes are generated and used as it is, it is important to save the prompt file. The prompt file should detail the reasoning behind using that particular prompt. Ideally, there must be an associated prompt file for every generative file.This way, if there arises a need to change the code in future, code maintainers can adjust the prompt (input) instead of altering the code (output).
Scenario 2: The codes are AI-generated but refined by human inputs
If the developer has generated a code but refined it further with his own inputs, it is essential to clearly demarcate the AI-generated codes from the manual edits. One way to do this is to add explicit comments to mark AI-generated and manually modified codes. Additionally, we can use version control to track the evolution of the code – AI-generated codes can be committed as the first version followed by manual edits as the subsequent version. Maintainers can thus follow the commit history to understand what was changed and why.
Scenario 3: Purely human-written codes
In scenarios where the codes are AI-free, it is equally important for the coder to explicitly state this. Explicit comments help the code readers comprehend the logic and nuances behind the codes. Clear documentation and comments create a bridge between the developer’s intentions and the code reader’s understanding, reducing potential confusion.
“AI-generated codes see success in applications with repetitive patterns and simple, well-defined contexts.”
This was the popular opinion at Trigent’s Gen AI session with the engineering teams. Developers pointed out that Gen AI excels in areas such as basic CRUD operations, simple form validations, and API integrations. Its strength lies in its ability to recognize patterns and generate code that mirrors existing structures, reducing the need for manual repetition.
“Very helpful to understand legacy codebases with poor to no documentation.”
Another area where generative ai solutions works magic is when there is a need to analyze old or unmaintained applications, which can otherwise be challenging to comprehend due to lack of documentation. Gen AI can quickly parse through the code and help developers comprehend the purpose and logic of existing structures, saving significant time and effort.
“Applications with modular architectures, where new modules can be replicated from existing ones, are good candidates for AI-generated codes.
However, even in this scenario, AI-generated codes work well only in simple straight-forward cases. Here is a finer analysis of the various operations, detailing where AI can help and where it needs human intervention.
| Focus Areas | Where AI Can Help | Where Human Skills are Essential |
|---|---|---|
| API Integrations | Generating boilerplate code for HTTP requests (GET, POST, PUT, DELETE). | Handling complex authentication flows (e.g., OAuth2 with refresh tokens). |
| Setting up headers and parsing JSON responses. | Managing rate limits, retries, and resilience against API errors. | |
| Simple APIs with well-defined documentation. | Transforming complex nested JSON data to fit the requirements of custom software development models. | |
| Writing basic webhook handlers. | Event-driven APIs requiring asynchronous workflows or message queues. | |
| Ensuring secure handling of API keys, tokens, and sensitive data. | ||
| Form Validations | Simple validation rules (e.g., required fields, min/max length, basic regex). | Handling cross-field dependencies (e.g., validating relationships between fields like “country” and “state”). |
| Generating client-side validation logic. | Validating against external datasets (e.g., checking usernames against a banned list). | |
| Automating repetitive form validation code. | Preventing injection attacks and ensuring secure data handling. | |
| Handling complex, dynamic forms with fields added or removed dynamically. | ||
| CRUD Operations | Generating basic database queries for Create, Read, Update, Delete. | Implementing custom business logic tailored to the unique needs of custom software development services projects (e.g., rules that depend on domain-specific calculations). |
| Writing boilerplate RESTful API endpoints. | Handling cascading actions (e.g., deleting a parent record and updating related child records). | |
| Setting up database migrations and models for simple schemas. | Optimizing queries for performance in large or distributed databases. | |
| Managing complex data relationships (e.g., one-to-many, many-to-many). | ||
| Legacy Codebases with Poor Documentation | Generating summaries or documentation for existing codebases by analyzing the code. | Understanding undocumented business rules or logic embedded in the code. |
| Auto-completing missing comments and annotations for functions, classes, and modules. | Debugging and restructuring poorly written or legacy code is a common challenge in custom software development services, requiring significant human oversight. | |
| Applications with modular architectures | Auto-generating boilerplate code for modules with clear interfaces or patterns. | Designing cross-module communication or resolving interdependencies. |
| Defining domain-specific responsibilities and ensuring separation of concerns. |
As Gen AI tools become increasingly context-aware, we can expect them to play a more profound and pervasive role across all areas of software development. They will seamlessly complement human expertise and enhance productivity while maintaining the integrity and originality of code.
“Pervasive but not intrusive”
In our ongoing custom software development services for enterprises across the globe, Trigent ensures that the use of Gen AI is pervasive, that is it’s not just used as part of coding applications but also building agents. In other words, we are coding with Gen AI assistants to build applications; we are using Gen AI assistants to build Gen AI agents; and we are creating Agentic AI platforms for enterprises and ISVs.
As generative AI technology becomes more pervasive and omnipresent, will it intrude with human intelligence? As Artificial General Intelligence (AGI) takes center stage, will we witness less and less of human intelligence at play?
These questions call for a separate discussion but to keep it short – novel solutions would remain incomplete without human intelligence, the kind that will help us to stay in focus, ask the right questions, and solve the bigger problems at hand. As Open AI CEO, Sam Altman, succinctly puts it, “Now that Gen AI can give you answers, figuring out what questions to ask has become all the more important”. And that, I believe, is just as crucial in custom software development services.
FAQs: The Impact of Generative AI solutions on Custom Software Development Services
1 How is Gen AI changing the landscape of custom software development services?
Gen AI is redefining custom software development by streamlining code generation, automating routine tasks, and assisting developers with intelligent recommendations. While it enhances productivity, custom software engineering teams must balance speed with maintainability and security to avoid accumulating technical debt.
2 Can Gen AI independently develop custom software from scratch?
Not entirely. Custom software development requires strategic planning, architecture design, and business logic implementation, which Gen AI alone cannot handle. While AI can generate boilerplate code and assist with repetitive tasks, experienced custom software developers must ensure efficiency, scalability, and security.
3 How does AI impact the quality of custom software projects?
AI improves efficiency but poses risks to custom software quality if misused. Studies show AI-generated code introduces 35% more technical debt due to redundant, copy-pasted solutions. Custom software teams must integrate AI strategically, using it as a tool rather than a complete substitute for human oversight.
4 How should custom software developers approach AI-assisted coding?
Developers should use AI as a pair programming assistant rather than an independent coder. Custom software engineers can:
- Use AI for instant code drafts, refining them iteratively.
- Employ AI as a learning tool to grasp complex custom software architectures.
- Automate routine tasks while focusing on high-value software design elements.
5 How does AI influence software security in custom software applications?
AI-generated code can introduce vulnerabilities if not reviewed properly. Custom software security best practices include:
- Manual code reviews to validate AI-generated logic.
- AI transparency markers to distinguish AI-generated vs. human-written code.
- Testing frameworks to detect security flaws in custom software projects.
6 Does Gen AI replace human ingenuity in custom software engineering?
No. AI excels at pattern recognition and repetitive coding, but custom software engineering involves critical thinking, problem-solving, and domain expertise. Custom software solutions must be tailored to business needs, requiring human insight for innovation and scalability.
7 How can businesses leverage Gen AI without compromising custom software quality?
Businesses should integrate AI pervasively but not intrusively into their custom software development lifecycle:
- Use AI to automate routine software engineering tasks.
- Implement AI governance to prevent over-reliance.
- Combine AI efficiency with human expertise for well-structured, maintainable custom software solutions.
8 What’s the future of Gen AI in custom software development?
As AI evolves, custom software development services will witness:
- Stronger AI-assisted debugging tools to improve code quality.
- AI-powered DevOps automation for efficient deployment cycles.
- Context-aware AI coding models that adapt to unique custom software needs.
9 How does Trigent ensure responsible AI use in custom software development?
At Trigent, we integrate Gen AI pervasively across custom software projects, ensuring:
- AI enhances but doesn’t replace human expertise.
- Transparent AI use with clear documentation and governance.
- AI-assisted coding that aligns with enterprise-grade software quality standards.
10 Why is custom software development still essential despite AI advancements?
AI can speed up coding, but custom software development ensures tailored solutions that fit unique business needs. While AI assists with automation, only custom software provides the flexibility, security, and scalability required for long-term success.
11 How can businesses ensure AI-generated code aligns with their custom software requirements?
Businesses should integrate AI as a tool, not a replacement, ensuring custom software maintains quality, security, and scalability. Regular code reviews, AI transparency markers, and human oversight help tailor AI-generated outputs to meet custom software needs effectively.
