The testing stage of software development and project management is crucial. It guarantees that the finished item meets the required quality standards and performs as planned. For effective project planning and execution, it is crucial to estimate testing efforts accurately.
This blog will examine the significance of estimation in software testing and the numerous elements that impact it. We will also cover the estimation techniques to increase the precision of testing.
These following types are widely used for estimation in software testing:
Let’s dive deep into each of these types of estimation in testing.
This technique involves seeking input from experienced QA professionals or subject matter experts with expertise in similar projects. They provide their insights and estimates based on their knowledge and past experiences.
For example, estimation in software testing is required for a feature on an ecommerce site, specifically the purchase functionality. The lead overseeing this task possesses specialized expertise in the relevant domain.
These experts must possess a profound understanding of the subject matter and pertinent experience. Their role involves furnishing estimations for the testing effort, and they also review estimations made by junior team members for accuracy and alignment with the project’s requirements.
This expert judgment process not only provides a holistic view of the testing effort required for the ecommerce platform but also ensures that the testing efforts are precisely tailored to the intricacies of ecommerce functionalities.
Leveraging the collective wisdom of QA professionals through this estimation process proves instrumental in delivering a high-quality and reliable ecommerce platform to end-users.
This technique includes using data from previous projects that are similar in nature or scope. By comparing the current project with past projects, estimators can make knowledgeable judgments about the effort and resources required for QA activities.
For example, let’s say that several months back, the team implemented a similar checkout enhancement for a different product category on the same ecommerce platform. The enhancement introduced a more user-friendly interface, additional payment options, and improvements to the overall checkout flow.
The QA team now reviews the historical data from the past project, specifically focusing on the testing effort invested in ensuring the quality of the checkout enhancement. This includes functional testing, usability testing, and security testing.
The team then recognizes the similarities between the previous and current checkout enhancements, such as the need for comprehensive usability testing and security assessments. They also acknowledge differences, such as the introduction of a new payment gateway in the current project.
Based on the team’s assessment, they quantify the comparison, estimating that the current checkout enhancement is 15% more complex due to the addition of the new payment gateway.
Applying an effort multiplier of 15%, the team adjusts the testing effort, recognizing that the new payment gateway introduces additional testing scenarios and potential integration challenges. Using the adjusted effort from the analogous comparison, the team estimates the testing effort required for the current checkout enhancement.
They also consider any unique aspects of the current project, such as the specific requirements of the new payment gateway. Analogous estimating in this scenario allows the QA team to make a realistic estimation for testing the checkout enhancement based on past experiences.
If, for instance, the testing effort for the previous checkout enhancement took two weeks, the team adjusts this to 2 to 3 weeks for the current project, factoring in the added complexity of the new payment gateway.
This type of estimation in software testing uses statistical models and algorithms to estimate QA effort based on specific parameters. It may consider factors such as the size of the project, the complexity of features, and the team’s productivity rates.
For this one, let’s say the team has historical data from previous sale events, and they need to estimate the effort required for performance testing during the upcoming major sale. The website’s infrastructure has undergone changes since the last sale, with additional servers and improvements in the caching mechanism.
Now, the team needs to
Parametric Estimating allows the QA team to make a data-driven estimation for performance testing, considering the specific parameters and historical data. If the historical data indicates that 100 hours were required for performance testing during a previous sale with similar parameters, adjustments are made accordingly.
This technique includes considering three estimates for each task: the positive estimate (best-case scenario), the negative estimate (worst-case scenario), and the most likely estimate.
These estimates are then combined using weighted averages or other mathematical techniques to calculate a more accurate estimate.
For example, the upcoming release includes changes to the user interface, the addition of new payment gateways, and modifications to the backend order processing system. The QA team recognizes the complexity introduced by these changes and the potential for unforeseen challenges.
The QA team optimistically estimates a scenario where the changes are smoothly integrated, and testing progresses efficiently. They estimate that regression testing might take 2 weeks under ideal conditions.
Recognizing potential complexities and unforeseen issues, the QA team provides a pessimistic estimate. In this scenario, they foresee challenges in integration and unforeseen defects emerging, potentially extending the testing period to 4 weeks.
Considering a realistic scenario where some challenges are encountered but not every aspect leads to delays, the team provides a most likely estimate. They gauge that regression testing might take around 3 weeks under typical conditions.
Apply Three-Point Estimation Formula:
E = (O + 4M + P) / 6
the Expected Duration (E) for regression testing.
2+4×3+4/6 = 18+4+4/6 = 26/6 = 4.33 Weeks.
The team discusses the results of the Three-Point Estimation. They recognize that the pessimistic estimate reflects potential risks and uncertainties. To mitigate risks, they plan to allocate additional resources, conduct thorough test planning, and implement contingency measures.
The Three-Point Estimation provides a comprehensive view of the regression testing effort, incorporating optimism, pessimism, and realism. The expected duration of approximately 4.33 weeks allows the team to plan resources, set realistic timelines, and be prepared for potential challenges.
This technique includes gathering input from multiple experts anonymously. An organizer collects and reviews their estimates and feedback iteratively, allowing experts to revise their estimates based on the information of others. This process of estimation in software testing continues until an agreement is reached.
For example, the security testing phase involves assessing vulnerabilities in the payment processing system, user authentication mechanisms, and data encryption protocols. Given the critical nature of security, the QA team recognizes the importance of accurate effort estimation.
The QA lead identifies a panel of security testing experts within the team, including individuals with expertise in penetration testing, encryption protocols, and overall cybersecurity.
Each expert provides an anonymous estimate of the effort required for the security testing phase. They consider factors such as the complexity of the payment processing system, the sensitivity of customer data, and the need for compliance with industry standards.
The QA lead collects the individual estimates, ensuring anonymity is maintained. The estimates are then summarized, and any extreme outliers are discussed with the respective experts to understand their perspectives.
The collated estimates are provided to each expert, along with a summary of the factors influencing the estimates. Experts are given an opportunity to revise their estimates based on the collective insights.
The process of providing estimates, summarizing, and allowing for revisions is repeated for several rounds. The iterative nature of the Delphi Technique allows for convergence toward a more accurate and consensus-based estimate.
After several rounds, a final consensus estimate is derived. This estimate reflects the collective judgment of the panel of experts, incorporating their diverse perspectives and insights.
In this real-time example, the Delphi Technique proves to be a valuable tool for estimating QA effort, especially in a domain as critical as security testing. By leveraging the expertise of a diverse panel of experts and allowing for iterative feedback, the QA team gains a comprehensive and well-informed estimate.
This technique includes breaking down the QA activities into smaller tasks or components and estimating the effort required for each task. The estimates are combined to provide an overall estimation for the entire QA process.
These are just a few examples of the techniques used for QA estimations. The choice of technique depends on the project’s context, available data, and the organization’s preferences and expertise.
It’s important to note that estimation is an iterative process and may require adjustment as more information becomes available throughout the project lifecycle.
For example, start by understanding the overall scope of the project, including the specific QA requirements and objectives.
In the software industry, QA estimations are often a complex process that draws from a mix of estimation techniques to ensure accuracy and reliability. The combination of different techniques, such as expert judgment, three-point estimation, bottom-up estimation, and the Delphi technique, allows software teams to create comprehensive and well-informed QA estimations.
By combining these techniques for estimation in software testing:
We do not employ all techniques uniformly or consistently. Instead, we utilize various combinations based on the specific project and its unique circumstances.
The mix of these techniques enhances the accuracy, completeness, and reliability of QA estimations. It allows software teams to consider historical data and expert judgment, balancing past experiences with insights.
The combination also addresses the inherent uncertainties and complexities of software development, making QA estimations more adaptable to changing project conditions.
In the software industry, where precision and adaptability play pivotal roles, this combination of techniques for estimation in software testing equips QA teams to make well-informed decisions. They become better at allocating resources efficiently and ensuring timely delivery of high-quality software products.
As for the preference for estimation techniques, many industry experts rely on expert judgment and analogous estimating, while some opt for hybrid approaches, blending various techniques for more nuanced estimations.
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