The Cost of Poor Software Engineering 

In today’s fast-paced business environment, software is a critical driver of success. However, poor software engineering practices can lead to substantial financial losses, missed opportunities, and frustrated users. According to a 2023 report from the Consortium for Information & Software Quality (CISQ), software failures cost U.S. businesses an estimated $2.41 trillion annually due to operational failures, security breaches, and maintenance expenses. Similarly, a study by McKinsey found that 17% of large IT projects fail so badly that they threaten the very existence of the company. 

Software engineering is the backbone of modern business, but even the most talented teams can fall into bad habits that slow progress, create hidden risks, and ultimately impact the bottom line. Whether you’re overseeing an internal team or working with an external partner, recognizing early warning signs can mean the difference between a high-performing engineering culture and a costly, inefficient one. Here are five red flags that signal trouble—and what you can do to course-correct before it’s too late.

#1 Frequent Bugs and System Instability

Red Flag: Your software experiences frequent crashes, glitches, or persistent bugs. 

Why It Happens: 

• Lack of automated testing 

• Poor error handling 

• Patchwork fixes instead of addressing root causes 

How to Address It: 

• Implement automated unit and integration testing 

• Enforce code reviews before deployment 

• Invest in robust error-handling mechanisms 

Seeing it in action (a Callibrity case study): 

An insurance company’s software development team, working in an offshore and onsite model, faced frequent bugs and deployment failures despite being skilled developers. The lack of a solid testing framework, inconsistent code reviews, and poor error handling led to inefficiencies, long development cycles, and frustrated customers. Automated testing was minimal, with developers writing manual tests, and integration tests were often unreliable. There was no formal peer review process, resulting in inconsistent code quality, bugs, and security vulnerabilities. Production issues were difficult to diagnose, and error messages were often generic, making debugging time-consuming. 

Steps Taken: 

To address this, the company consulted experienced professionals and decided to implement the below steps 

• Unit Testing and Integration Testing
   
  • The team implemented test-driven development (TDD) for new features, refactored existing code, and used tools like JUnit to write unit tests for individual functions and methods,  JaCoCo to ensure that a high percentage of the codebase was covered by unit tests, Postman to do integration testing. 

• • The team configured a CI/CD pipeline (using Jenkins and GitLab CI) that ran automated tests on every pull request (PR) before merging into the main branch. 

• • Tests were executed in a dedicated test environment that mimicked production conditions, including database connections and API calls. 

• Code Review Guidelines and Process
   
  • The team created a set of best practices and guidelines for code reviews, including standards for readability, design patterns, and testing. 

• • They used GitLab pull requests (PRs) to facilitate the code review process. Developers were required to submit PRs for any code changes, including bug fixes, features, or refactoring. Each PR was reviewed by at least two other team members before being merged. 

• • A checklist was used to ensure that important aspects of the code (e.g., error handling, security practices, and performance) were thoroughly reviewed. 

• • To ensure a wider range of feedback and better knowledge distribution, the team rotated reviewers periodically. 

• Error Logging / Error Handling Framework / Monitoring
   
  • The team adopted structured logging using tools like Logback. Logs included essential metadata, such as error codes, timestamps, and stack traces. 

• • Logs were sent to a centralized logging service to Splunk, which made it easier to search and analyze logs from all services. 

• • A global exception handler was created for uncaught exceptions, ensuring that the application could gracefully handle errors without crashing. 

• • The team integrated a real-time monitoring system like Datadog to capture application errors, track performance, and generate alerts. 

• • Dashboards were set up to monitor error trends, system uptime, and other key metrics, allowing the team to act proactively. 

As a result, the company saw: 

• Reduced the number of bugs in production by 30% and Error detection and resolution time decreased by 50% in the first quarter. 

• Enhanced customer satisfaction due to more stable and reliable applications. 
  

#2 Difficult-to-Maintain Codebase 

Red Flag: New feature development is slow because the code is hard to understand and modify. 

Why It Happens: 

• Lack of clear structure and coding standards 

• “Spaghetti code” that lacks organization 

• Failure to regularly refactor code 

How to Address It: 

• Standardize coding practices across teams 

• Schedule routine code refactoring to improve readability 

• Utilize version control and documentation best practices 

#3 Repeated Project Delays and Cost Overruns 

Red Flag: Software projects consistently miss deadlines and exceed budgets. 

Why It Happens: 

• Poor project planning and scope creep 

• Inefficient communication and unclear requirements 

• Excessive rework due to flawed initial designs 

How to Address It: 

• Adopt agile methodologies for incremental development 

• Define clear project scope and expectations upfront 

• Ensure regular stakeholder alignment to prevent misdirection 

#4 Slow Performance and High Latency 

Red Flag: Your software takes too long to load, causing user frustration and productivity loss. 

Why It Happens: 

• Unoptimized database queries and algorithms 

• Poor memory management leading to resource drain 

• Lack of scalability considerations in early design stages 

How to Address It: 

• Conduct regular performance audits and optimizations 

• Optimize queries and improve data caching mechanisms 

• Design with scalability in mind for future growth 

#5 Lack of Ownership and Accountability 

Red Flag: Teams lack clear responsibility, leading to finger-pointing and stalled progress. 

Why It Happens: 

• Undefined roles and responsibilities 

• Weak collaboration and siloed teams 

• Insufficient leadership and oversight 

How to Address It: 

• Clearly define roles and accountability measures 

• Foster a culture of transparency and regular status updates 

• Ensure strong leadership to guide software initiatives 

Key Takeaways for Business Leaders 

To prevent poor software engineering from derailing your projects, prioritize these best practices: 

• Implement rigorous testing and code review processes to catch defects early. 

• Standardize coding guidelines and documentation to improve maintainability. 

• Adopt agile methodologies to enhance flexibility and efficiency. 

• Monitor system performance regularly to avoid slowdowns. 

• Invest in proactive technical debt management to prevent long-term issues. 

Wrap Up

Poor software engineering isn't just a technical problem—it’s a business risk that affects costs, productivity, and customer satisfaction. By recognizing the warning signs early and enforcing best practices, you can ensure your software remains scalable, efficient, and aligned with your strategic goals.  

A few small changes can make a big impact—discover how to turn things around.

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