A Survey on Modern Techniques in Software Testing

Abstract

Software testing is a critical phase in the software development lifecycle that ensures the delivery of reliable, defect-free software. With the increasing complexity of software systems, traditional testing methods are no longer sufficient to meet quality requirements. This paper presents a comprehensive survey of modern techniques in software testing, including automated testing, model-based testing, mutation testing, and machine learning-aided testing. We compare these approaches in terms of effectiveness, efficiency, and applicability across different software domains. Finally, we discuss open challenges and future research directions.

Introduction

Software testing is essential for ensuring that software systems meet specifications and user expectations, especially as system complexity increases and development cycles shorten. Traditional manual testing is increasingly inadequate due to high cost and limited effectiveness, leading to the adoption of automated and intelligent testing approaches.

The paper reviews major modern software testing techniques. Testing is broadly categorized into static testing (such as code reviews and static analysis) and dynamic testing (executing test cases). With the rise of agile development and CI/CD pipelines, automation and advanced techniques have become critical for maintaining software quality at speed.

Key testing techniques discussed include automated testing, model-based testing (MBT), mutation testing, and machine learning–aided testing. Automated testing uses tools like Selenium and JUnit to reduce manual effort and support frequent regression testing, though it requires initial setup costs and skilled personnel. Model-based testing derives test cases from behavioral models, improving coverage and early fault detection but demanding significant effort to build accurate models. Mutation testing evaluates test suite effectiveness by introducing code changes, offering precise quality assessment but suffering from very high computational cost and poor scalability. Machine learning techniques support test prioritization, defect prediction, and test data generation, improving efficiency and scalability, though they may produce false positives.

A comparative analysis shows that while MBT and mutation testing offer very high coverage, they are costly and resource-intensive. Automated and ML-aided testing provide better scalability and balanced cost-effort trade-offs.

The paper concludes by highlighting ongoing challenges in software testing, including the test oracle problem, high false positives—especially in ML-based tools—and scalability issues for large systems. Future research should focus on addressing these challenges to further enhance intelligent and scalable testing solutions.

Conclusion

Modern software testing techniques improve quality and efficiency compared to traditional methods. Automated and intelligent testing approaches are essential to cope with the evolving complexity of software. However, challenges such as test oracle issues and scalability remain open

References

[1] G. J. Myers, C. Sandler, and T. Badgett, The Art of Software Testing, 3rd ed., Wiley, 2011. [2] B. Beizer, Software Testing Techniques, 2nd ed., Van Nostrand Reinhold, 1990. [3] P. Ammann and J. Offutt, Introduction to Software Testing, Cambridge University Press, 2016. [4] A. Bertolino, “Software Testing Research: Achievements, Challenges, Dreams,” 2010 Future of Software Engineering (FOSE), 2010, pp. 85–103. [5] L. C. Briand, Y. Labiche, and P. Omar, “A UML-based Approach to System Testing,” ACM Trans. Softw. Eng. Methodol., vol. 15, no. 1, pp. 1–38, 2006. [6] M. Harman et al., “Search-Based Software Engineering: Trends, Techniques and Applications,” ACM Comput. Surv., vol. 45, no. 1, 2012.

Copyright

Copyright © 2026 Sneha B. Sahane. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.