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Automated Software Tools

Methods of automated software tools with manual review processes

Automated software tools with manual review processes are becoming increasingly popular in many industries as they balance efficiency and accuracy. These tools typically involve using a computer program to perform a specific task, such as analyzing data or identifying patterns, followed by a human review to validate or correct the results. The automated method is the first step of the process, and it refers to the use of computer programs or algorithms to perform a specific task or process. For example, a software tool may use machine learning algorithms to analyze data and identify patterns or anomalies.

According to the author (Marshall & Wallace,2019), this step is done by the computer, and it is usually the fastest and most efficient way to process large amounts of data. However, the computer cannot replace human judgment; therefore, the results are subject to human review. The manual review method is the second step of the process, and it refers to having a human review and validates the automated process results. For example, a human may review the results of the data analysis performed by the software tool and make any necessary corrections or adjustments.

Despite this step being done by a human, it is usually slower but more accurate. The human reviews the data and ensures that the results are accurate and reliable, thus ensuring that the outcome is of high quality. Together, these two methods balance efficiency and accuracy, with the automated process handling the bulk of the work while the manual review ensures that the results are accurate and reliable (Marshall & Wallace,2019). The approach is particularly useful in situations where large amounts of data need to be analyzed quickly but where accuracy is critical, such as in finance, healthcare, and other industries.

It is important to note that the manual review process can be performed by humans or by AI models. In the latter case, it is called AI-assisted manual review. It is a combination of both methods, where the AI model helps the human reviewer by providing suggestions, highlighting potential errors, or even doing some of the repetitive tasks. (Munn et al.,2019) states that approach can speed up the manual review process, enhance the accuracy and consistency of the results, and reduce the human workload.

What criteria did you use to distinguish between the two methods?

The criteria used to distinguish between the two methods of automated software tools with manual review processes would likely depend on the research or analysis’s specific context and goals. However, some factors that could be considered when making such a distinction include the degree of automation. One method may involve more automation than the other, meaning that the software tool performs more tasks or processes without human intervention.

The level of human involvement is because the other method may involve more human involvement, meaning there are more opportunities for manual review or oversight during the process. Additionally, the type of task or process performed can be a criterion since the methods may be better suited for different tasks or processes, such as tasks that require a high degree of accuracy or tasks that involve sensitive or confidential information.

The main advantage of automated software testing is that it is faster and more efficient than manual testing. Automated tests can be run quickly and repeatedly, allowing for more thorough testing and faster identification of bugs and other issues (Vidgen & Yasseri,2020). Additionally, automated testing can be performed on a large scale, making it well-suited for large, complex software systems. On the other hand, manual testing has its advantages. One of the biggest advantages of manual testing is that it allows for more detailed and thorough software testing. Manual testers can use their intuition and experience to identify issues that automated tests may not detect. Additionally, manual testing allows for more flexibility in the types of tests that can be performed, as manual testers can perform tests that are not easily automated.

Two main Static Testing Techniques and the four different types of reviews included in static testing

Static testing denotes a software testing method that involves analyzing the code without executing it. The two main static testing techniques are code reviews and static code analysis. There are two main static testing techniques, white box testing, and black box testing. White box testing is a type of testing where the internal structure of the code is known and tested (Durieux et al.,2020). White box testing is also known as glass box testing or clear box testing. On the other hand, black box testing is a type where the code’s internal structure is unknown, and only the external behavior is tested.

Static testing includes four different reviews: walkthroughs, inspections, technical reviews, and peer reviews. Walkthroughs involve a group of people reviewing the code together, with one person as the “leader” and pointing out any issues or areas for improvement. Inspections involve a more formal process, where a group of people reviews the code, and a checklist of items to be reviewed is used (Tahir et al.,2021). Technical reviews involve a more in-depth examination of the code, usually by someone with a technical background. Peer reviews involve the code being reviewed by other development team members.

Static code analysis is a technique where the code is analyzed by a tool to identify any issues or potential improvements. The tool checks the code against a set of rules and can automatically identify issues such as coding style violations, potential bugs, and security vulnerabilities. It is important to note that both code reviews and static code analysis are complementary, and using both techniques together can provide a more thorough analysis of the codebase.

Validation of the output results for the two methods

You can use various techniques to validate the output results for the two methods. For automated software tools, you can use metrics such as code coverage and defect density to measure the tool’s effectiveness. You can also compare the results of the automated tool with the manual review to ensure that the tool finds the same defects as the manual review (Paiva, Leal & Figueira,2022). On the other hand, for manual review, you can use techniques such as peer review, walkthroughs, and inspections to validate the output results. You can also use metrics such as the number of defects found per hour of review to measure the effectiveness of the manual review process.

Additionally, validate the output results for automated software testing. The expected results can be compared to the results obtained from running the test. The results can be validated for manual testing by reviewing the documentation and test plans and discussing the results with the manual tester. Additionally, manual and automated testing can validate the results and ensure that the software functions as intended.

In conclusion, both automated software tools and manual review processes have advantages and disadvantages. Automated software tools are efficient and can be easily integrated into the development process, but they may not find all types of defects and may produce false positives (Paiva, Leal & Figueira,2022). Manual review is a thorough process that can find defects that automated tools may miss, but it can be time-consuming and subject to human error. The choice of which method to use depends on the specific needs of the project and the resources available.


Bitkina, O. V., Kim, H. K., & Park, J. (2020). Usability and user experience of medical devices: An overview of the current state, analysis methodologies, and future challenges. International Journal of Industrial Ergonomics76, 102932.

Durieux, T., Ferreira, J. F., Abreu, R., & Cruz, P. (2020, June). Empirical review of automated analysis tools on 47,587 Ethereum smart contracts. In Proceedings of the ACM/IEEE 42nd International conference on software engineering (pp. 530-541).

Marshall, I. J., & Wallace, B. C. (2019). Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Systematic reviews8(1), 1–10.

Munn, Z., Aromataris, E., Tufanaru, C., Stern, C., Porritt, K., Farrow, J., … & Jordan, Z. (2019). The software development to support multiple systematic review types: the Joanna Briggs Institute System for the Unified Management, Assessment, and Review of Information (JBI SUMARI). JBI Evidence Implementation17(1), 36-43.

Paiva, J. C., Leal, J. P., & Figueira, Á. (2022). Automated assessment in computer science education: A state-of-the-art review. ACM Transactions on Computing Education (TOCE)22(3), 1-40.

Tahir, S. I., Chikh, A., Tounsi, A., Al-Osta, M. A., Al-Dulaijan, S. U., & Al-Zahrani, M. M. (2021). Wave propagation analysis of a ceramic-metal functionally graded sandwich plate with different porosity distributions in a hygro-thermal environment. Composite Structures, p. 269, 114030.

Vidgen, B., & Yasseri, T. (2020). Detecting weak and strong Islamophobic hate speech on social media. Journal of Information Technology & Politics17(1), 66-78.


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