Mental Models: Ultimate Guide To Make Intelligent Decisions

OVERVIEW

A mental model is the specific thought process that helps analyze the problem. There are many kinds of known mental models, each offering distinct perspectives on a complex concept, aiming to simplify it. Simply, it represents the mind's methods of understanding and clarifying something.

These models are essential for software developers and testers to develop high-quality software applications. In other words, these mental models are the conceptual frameworks that professionals use to understand, modify, and analyze the software.

The mental models are constructed through the experience, knowledge, and skills of software professionals. These professionals gather a deep understanding of software systems over time, and this knowledge forms the basis of their mental models, enabling them to work effectively in the software development and testing phase.

The mental models are leveraged in software testing and development. This is because there is a demand for innovation, new ideas, and exciting features in software applications. However, the development of such software applications depends not just on a line of code or test case but also on the mental model.

It acts as the analytic framework to understand better software, its complexity, and its way of ensuring quality in less time. These models help software professionals interpret how the software functions, how it should behave, and how to approach tasks like designing, coding, testing, and debugging.

Mental models in software engineering represent the conceptual knowledge and understanding of the software professionals on the application, development, and test process and system they are working on. With this, it becomes easy for developers to make informed decisions, fix bugs quickly, and establish good communication with the team. Thus, the mental model serves as a foundation for implementing innovations in software development and focuses on higher-level problem-solving rather than getting lost in the minutiae of every detail.

Thus, they give a simplified view of reality, allowing the team to reason and predict outcomes efficiently. This guide focuses on exploring mental models in detail, understanding their key concepts, types of mental models, and others. This will help you to apply mental models in different software testing and development contexts.

Let us get started by first understanding what mental models are. ?

What is a Mental Model?

A mental model represents understanding how software or other things must operate. In other words, mental models represent a thought in an individual's mind to understand and conceptualize complex system/software applications, their concepts, and processes in software development. It is a simple approach that helps testers and developers to understand complex software architecture, algorithms, and more.

They are like a toolbox of patterns and abstraction that software developers use to mentally represent various aspects of how the software applications must interact with its components, the way the data flows, and the different responses of the applications towards multiple inputs.

This concept applies to any thought, idea, or belief. Because it's hard to remember all the details, software developers use these models to organize the complexity of the software application development process into clear and structured parts. Having a mental model in hand, software professionals can predict how exactly the software applications will behave in different test scenarios and environments. Based on this, they can make informed decisions and fix any errors or bugs in the software applications.

Within software testing and development, where many programming languages and patterns exist, it becomes essential to understand the subject matter and be aware of the broader context. This requires a mental model clarifying the connections among various software testing technologies. This mental model serves as a map, helping software professionals understand how different software testing technologies and practices work together smoothly.

How Mental Models Influence Decision-Making

Now, let us see how mental models are so important to software professionals when making a crucial decision in the Software Testing Life Cycle (STLC) in less time.

For instance, think of a situation where you find a problem in your code. There are various ways to solve issues in the code. Like, testers can look at the requirements, test the code, and check for features that aren't working right by examining the log file. However, this method can take a lot of time and demand more testing work simultaneously.

Instead of resorting to a mixture of different test strategies, you can delve into the mental model of software application development. They act as a guide and help to navigate the decision-making process.

• The testers could evaluate the circumstances that led to the bug and pinpoint areas where the code diverges from the expected outcome of your cognitive understanding.
• Remarkably, even without a Google search, a developer can dig potential solutions through this method. Understanding how the software applications should work helps you develop ideas for fixing the issue.

For example, let's say you have a mental model of how databases work and another mental model for optimizing code performance. When you encounter a situation where your software application is slow due to database queries, you can use these mental models to guide your decision-making. You can optimize the database queries or implement caching strategies based on your mental models.

Here are the key points on how the mental model impacts the decision-making process:

• The mental model allows the software professionals to conceptualize the issue in the test process better and gives them a structured way to verify data and information to fix them.
• The software professional uses a mental model to filter out the information and prioritize it to assess its impact on software development. For example, when a software tester is debugging a complex issue, their mental model helps them filter out unrelated code and system components, focusing on the specific parts that might be causing the errors.
• Mental models enable software professionals to predict the outcomes of their decisions. For example, they can quickly see how the changes made to the code or infrastructure impact the function of the software applications.

Significance of Mental Models in Software Engineering

Mental models hold a significant role in software engineering, and their importance comes from the various aspects explained below

• Managing complexity

Software applications can be complex, with multiple components, interdependencies, and interactions. They benefit software engineers by simplifying this complexity into more manageable segments based on their understanding of the software applications and their codebase.

• Problem-solving efficiency

They give software engineers a systematic framework for tackling issues by helping them organize and analyze them more methodically. Whether dealing with a software glitch, performance bottleneck, or design issues, engineers can use this model to pinpoint potential causes and solutions quickly.

• Guiding design and architecture

In software design, these models serve as an approach to guide architects. They assist engineers in visualizing how a system should behave and perform even before commencing the coding process.

• Enhanced communication

It serves as a common language within development teams. When team members share a standard mental model of the system, It helps effectively convey complex ideas and concepts.

• Adaptability

Engineers with robust mental models can seamlessly adapt to emerging technologies, languages, and patterns because they grasp the foundational principles.

Accelerating Problem-Solving Through Mental Models

Mental models expedite problem-solving by presenting a structured methodology. Below, we will discover some methods that can help resolve problems quickly.

• Pattern recognition

Software engineers leverage mental models to find the differences and similarities for the identified errors or patterns in software applications based on their past working experience on similar applications. This allows them to fix issues efficiently, which has been proven effective without wasting time searching for new solutions.

• Efficiency

Mental models enable software engineers to promptly identify the underlying causes of errors in the functionality of the software applications. Rather than resorting to trial and error, they can methodically detect issues, saving time and resources.

• Informed decision-making

When confronted with choices in software design or code optimization, like deciding which algorithms to use, choosing the overall structure and organization of the software, or making choices about coding conventions and style, mental models give software engineers the tools to make informed decisions grounded in a deep understanding of how each choice will impact the software application.

• Fostering creativity and innovation

While mental models provide a structural foundation, they also nurture creativity. Software engineers can combine and adapt their mental models to devise innovative solutions to intricate issues in complex software applications.

Now, you may wonder which mental models can help overcome challenges. In the next section, we present several noteworthy mental models explicitly tailored for developers, serving as valuable tools to complete their tasks.

Type of Mental Models

A mental model represents how we understand a concept. These mental constructs are part of our everyday conception, often operating subconsciously. However, once we know their purpose and application, mental models transform into valuable assets for organizing complex issues, enhancing concentration, and developing superior software applications. Here are the lists of the mental models that the software engineer can use during software development and testing.

User Modeling

It is also known as emulation or simulation, which shows how an individual behaves or acts in certain situations. In terms of software development, this model pertains to how the software professionals have interacted previously with the software applications. Based on this, data is collected and recorded to build a very useful model for the team to predict the performance, issues, and ways to fix them during software development.

For example, when developing a web application, knowing how the users will interact with the interface of the application based on previous experience with similar applications, the developers will build user-friendly web applications where there is less chance of testing every possible option and layout. Thus, this helps rapid design iteration and requires less development and testing time and effort. However, testing is an important and integral part of any software development process. You can also test how your mobile and web application behaves on various platforms or devices by leveraging cloud platforms like LambdaTest.

LambdaTest is an AI-powered test orchestration and execution platform that lets you run manual and automated tests at scale with over 3000+ real devices, browsers, and OS combinations that can help you ensure that your software application works perfectly across various platforms and browsers. It also offers mobile testing labs where you can test mobile apps using emulators and simulators and a real device testing cloud.

You can further reduce the test execution time with automation testing. It is particularly helpful in complex projects where you have to deal with multiple repeated test cases and tight time constraints. Watch this video on how to perform automation testing on LambdaTest.

Subscribe to the LambdaTest YouTube channel for more videos on Parallel testing and Selenium testing and to enhance your testing experience!

Rubber Ducking

This is often called "rubber duck debugging," which got its name from an interesting story. The tale goes that a programmer once described their code line by line to a rubber duck. While this might seem unusual, the idea behind it is pretty simple. When you explain your code to someone else, even if that someone is an inanimate object like a rubber duck, it helps you uncover issues and pinpoint where you're facing difficulties. It encourages you to think more creatively.

As you walk through your code, you gradually reach the point where you go off track or make a mistake. It's essential to note that you don't necessarily need an actual rubber duck or a toy for this technique to work. A "rubber ducking" session with a colleague or a friend can be just as helpful. When you explain your code, they might offer insights and brainstorm potential solutions as you discuss the issues. Verbalizing your thoughts and code can often lead to valuable insights and help you find answers to programming problems.

Circle of Competence

Software professionals use the circle of competence model to decide which software project to work on, what technology to use, and how to focus their efforts. This is based on their knowledge, skill, and expertise, including a programming language, development methods, testing frameworks, etc. Therefore, the circle of competence model highlights that software professionals should take projects that align with their team's expertise and skills.

It is well-known that no matter how long a software developer has been in the development domain, he won’t know everything about different projects. Say, for example, a mobile app developer moves on as a developer in the finance industry; he has to be skilled in C# and C++, user interface design, etc. Some of the skills might be useful for his current designation, but he must also know about bank law and security services. Using the circle of competence model, developers can easily find the challenges they can encounter in the future. They can identify the scope of improvement and need to learn new skills or seek help when they find themselves outside the circle.

Feedback Loops

The feedback loop is the fundamental mental model based on the principle of continuous learning from gathered information or feedback and a way to improve the quality of software applications. It takes place in a Plan-Do-Check-Act cycle, which comprises four basic steps:

• Plan: Find what needs to be done.
• Do: Follow the initial project.
• Check: Evaluate the execution of the test plan and verify its effectiveness.
• Act: Applying the plan into action.

This feedback loop model is leveraged during the development phase. Suppose you are involved in developing a web application, and during its development phase, you release the alpha version of the app to beta testers for feedback. By gaining feedback, it becomes easy to identify bugs, usability issues, or feature requests. Based on the feedback, developers implement the suggestion, giving new beta versions. This continuous feedback loop makes your application more reliable and user-friendly and reflects the fundamental nature of the software development process.

The significance of the feedback loop model lies in its ability to uncover defects early in the Software Development Life Cycle (SDLC), leading to cost-effective bug fixes. It allows software professionals to adapt software per users' requirements and learn and implement the skills in future software projects.

Mindmap

A mindmap serves as a visual representation illustrating concepts or ideas. It also reflects their relationship, which initiates from the central idea and goes into subtopics.

To start developing a software project, consider beginning with a mind map. Initiate with a central idea, which could be the primary challenge in developing software applications. Following this, you may incorporate subtopics linked to the central idea. The main idea is the critical responsibilities allocated to each team. Or, it could also be the feature of a software application under test. Related to it, the subtopic could be test cases, test data, and testing environments. You can then extend these subtopics or branches, encompassing tasks designated to individual team members, all contributing to the overarching objective.

In software development, a mind map proves valuable during the testing phase. Testers can utilize it to delve into an application's features and document successful and unsuccessful test results. Further, this visual representation helps testers plan effectively and allows for better communication among team members, as everyone can quickly grasp the testing strategy. Throughout this process, you can even include inquiries within the sub-branches. This approach ensures that feedback and issues are systematically organized for easy comprehension.

Using the mental mind map model, software professionals can:

• Simplify complex information.
• Supports detailed and easy understanding of software application's different components.
• Identify issues and risks associated with software applications.
• Find a different testing approach.

Hill Charts

Hill charts are a significant mental model for tracking the progress of software application development and effective management of workload. It is a visual representation that resembles a hill, with the vertical axis indicating progress from bottom to top and the horizontal axis representing time from left to right. Initially, a task or user story resides at the base of the hill, denoting zero progress.

As development testing of software applications begins, it ascends the hill, reaching its peak upon completion. An example of applying hill graphs in software development is during an agile sprint. Each user story or task is depicted as a hill graph, and team members regularly update them to visualize progress. This visualization encourages transparency, simplifying the identification of tasks that may be lagging or facing obstacles.

Having hill charts, developers and testers get a clear view of the workload and activities, thus allowing them to identify the issue quickly. They also use them to make to-do lists for their software project. In addition, developers working on multiple software projects with large team sizes utilize hill charts to measure the team's focus area and find any obstacles and ways to move ahead.

Parkinson’s law

Parkinson's law has a significant value in software development and testing because it guides software professionals in knowing how exactly the workload expands to meet the given time for the release of software applications. This law suggests that to meet the deadline for the completion of software projects, increase the amount of work needed for the development and testing activities, no matter if the activities can be completed before the deadline.

Suppose a development team was given four weeks to add new features in the software application that allow easy user login. Without time management and a test plan, the team will likely find four weeks sufficient and utilize the entire time to complete the given task. However, the work could have been completed in less than four weeks. However, due to poor management, the team may start slow and end up knowing that more issues arise after making the changes and require more time to complete the test process.

In the above scenario, Parkinson's law guides the team in setting deadlines for maximum efficiency. Using this law, the team can set shorter and more realistic deadlines to have better work practices. For instance, breaking the project into smaller, time-bound iterations (e.g., sprints in agile development) can create a sense of urgency and prevent unnecessary time expansion. Thus, the significance of Parkinson's law in software testing is that it highlights the importance of time management.

5 Whys

The 5 Whys method is a problem-solving strategy wherein you repeatedly inquire "Why?" five times to dig the root cause of an identified issue in software applications. Using the 5 Whys can facilitate an in-depth comprehension of a user's thought process and the identification of the underlying reason behind a problem through a sequence of "why" queries.

An illustrative scenario that resonates with developers could be as follows:

• Why was the calendar feature in the app inaccessible to the user? The recent update contained a bug.
• What contributed to the emergence of the bug in the recent update? The team needed help in comprehensively testing all the features.
• Why did the team face challenges in testing all the features? The newly onboarded testers needed help to conduct thorough feature assessments.
• What led to the subpar performance of the new testers? They needed to be more adequately equipped with resources and needed proper training.
• Why were they not provided with adequate training and resources? A significant portion of the new testers was working remotely.

The team responsible for their training encountered challenges due to the need for a well-established onboarding process tailored for fully remote workers.

The reasoning behind this approach is that when an issue is identified, the most apparent solution may not effectively tackle the fundamental issue at hand. Recognizing the primary cause allows developers to economize their time and exertion. Such identification is necessary for them to resort to quick fixes while the genuine issue persists unattended.

The significance of the 5 Whys in software testing and development lies in its ability to find the main reasons behind issues or defects. This method promotes a systematic approach to problem-solving, ensuring that solutions target the root cause rather than just addressing surface-level issues.

Inversion

While addressing challenges, our natural tendency often gravitates towards forward-looking approaches. This approach can be practical when dealing with straightforward issues but can present significant obstacles when faced with intricate problems that necessitate a different perspective.

Conversely, the model of inversion offers a valuable alternative. It encourages us to dissect complex issues by considering them reversely and providing innovative solutions. In other words, inversion models allow software professionals to not directly think about how to achieve a desired outcome during the development of software applications but instead direct them to address how to prevent any failure or unexpected outcome. It suggests approaching any challenge in software testing by considering the negative view.

Suppose a software team is involved in designing a new user authentication system for a web application. The team may think of having a smooth login procedure for the user. But with the use of the inversion model, the team needs also to ensure that there is no unauthorized access. This change in the team's perspective allows them to identify any security errors like weak password policies or data breaches.

Thus, by following this inversion approach, QA analysts and software developers may uncover the potential risks and failures in the Software Development Life Cycle. It encourages a proactive approach to problem-solving, emphasizing prevention rather than reacting to issues after they occur.

Occam’s Razor

Occam's Razor, also called the principle of parsimony, is a conceptual framework used in problem-solving. This framework says that the simplest one is often the best choice when you have different ways to solve a problem. It's like saying "keep it simple" in problem-solving. It advises software professionals to avoid any unwarranted difficulties.

Imagine a software developer who can create a program in two ways: one is straightforward to understand, while the other is complex and hard to follow. Both methods produce the same result, but the simpler code is preferred.

Although the outcome remains consistent, the more direct solution proves more straightforward to implement and yields long-term advantages. Consequently, Occam's Razor advocates simplicity over complexity unless compelling evidence suggests otherwise.

Pareto Principle

The 80-20 principle, commonly recognized as the Pareto Principle, is a well-known concept. It says that roughly 80% of the consequences (or results) are derived from approximately 20% of the factors (or inputs) influencing a particular event. In software testing, it's frequently observed that a limited number of defects or functionalities are accountable for most issues or value generation. This principle is instrumental in task prioritization and efficient resource allocation.

Imagine a software testing team working on improving the performance of software applications. Their job is to find and fix problems. They apply the Pareto principle and discover that fixing just 20% of the issues can solve 80% of the software application's crashes and instability. So, they concentrate on resolving that critical 20% of the problems to make the software applications more stable and reliable.

The significance of the Pareto Principle in software testing and development is that it encourages teams to focus on what truly matters and where their efforts will have the most significant impact.

Econ 101

The principle of Econ 101 is mainly related to supply and demand, and its relation to software development and testing is understanding the dynamic of resource allocations and user requirements. To better understand this model, let us consider a scenario where the team involved is working on multiple software projects with limited availability of resources. In this, supply is defined as the availability of developers, testers, and other resources like hardware, software, etc. On the other hand, demand is related to different software projects needing the above resources.

In the above scenario, if the demand for the resource is more than the available supply, there might be delays in project timelines. It could also lead to increased workload and issues in the software applications. In contrast, if the supply exceeds demand, resource underutilization and inefficiencies may occur.

The significance of applying the supply and demand model in software testing and development is its ability to help teams make informed decisions about resource allocation, project prioritization, and managing workloads.

The DRY Principle (Don't Repeat Yourself)

It is one of the most crucial mental models in software testing and development. It focuses on removing any code duplication and promoting code reusability. It is defined that every piece of knowledge and information must have a single precise place within a software system. This model helps the software developers avoid writing code with duplicated code, data, and logic. This is because redundancy is responsible for errors in the software application and makes any future update a challenging task.

Suppose a software development team builds a website to calculate the total cost involved in the sale of products. Generally, teams may write code to initiate the calculation separately in different components of the applications. However, this may lead to redundancy in code. Therefore, they apply the DRY principle and develop a centralized function or module responsible for calculating the total orders. It will help ensure consistency in the calculation, and any changes or updates to the pricing logic must only be made in one place. Thus, the significance of the DRY Principle in software testing and development is that it leads to more maintainable, efficient, and reliable codebases.

Type 1 vs. Type 2 Decisions

Distinguishing between Type 1 and Type 2 decisions (often called one-way and two-way door decisions) involves recognizing their permanence. Type 1 decisions are irreversible, warranting careful consideration and deliberation. Conversely, Type 2 decisions can be readily reversed, so there's no need for excessive attention.

For instance, choosing between a SQL and NoSQL database leans more towards being a Type 1 decision. Once your data is structured within a specific database system, changing it becomes a complex and non-trivial journey. On the other hand, deciding whether to use Axios or Fetch for retrieving data in a React application is closer to a Type 2 decision. You have the flexibility to switch between these libraries without much difficulty and can transition gradually if needed.

Conway’s Law

Conway's Law states that a software system's configuration often imitates the design of the teams and organization responsible for its creation. In simpler terms, the structure of a software system often reflects the communication and organizational structure of the team that built it. Improved organization leads to enhanced design. If we investigate software teams, they are structured around the technical capabilities of their members. There are front end engineers, backend engineers, database administrators, and DevOps professionals, among others.

Let us understand this model with an example. Suppose there are two different teams

• Team A: This team of one organization works in isolation from the development team due to different geolocations. They rarely communicate with each other and report the work to different managers.
• Team B: This team of another organization has both a software testing team and a development team working together in the same location. They meet daily in the development progress of software applications and make collaborative decisions if needed.

Conmay’s law, the software application developed by team A may have an integration issue because the testing team may not understand the code generated by a different development team. While the software application developed by team B, it is less likely that the software application they developed may have any error. It is because there has been continuous communication that helps to understand the errors and fix them more effectively.

The resulting software product will mirror how these teams communicate. In a software team, it's crucial to observe their communication and organizational methods, as they will directly influence the similarity in structure and quality of the software they generate.

How To Choose the Right Mental Models?

In the above explanations, you have encountered different mental models that can be used in software development and testing. However, software professionals may face challenges choosing a suitable mental model for their software development strategy. Here are some tips that can be followed to select the suitable mental model

• First, you should identify the issue and goal of the software project. It will help you underline the outcome the team expects from the test process. All this will guide you in choosing the suitable mental model that aligns with your objective. For this, you need to review each model carefully.
• Consider factors like complexity, available resources, time constraints, and the type of error encountered during software testing. It will guide the match to an appropriate mental model that suits the above factors.
• You should familiarize yourself with different mental models that could apply to your situation. It may involve research, seeking expert advice, or drawing from your experience.
• You should collaborate with colleagues, mentors, or peers to gain different perspectives on the problem and potential mental models.
• It is always preferred to monitor the effectiveness of the chosen mental model. If it does not yield the desired results, be prepared to pivot and explore alternative models.

Common Mistakes When Using Mental Models

Software professionals using mental models often make mistakes that may cause challenges in the software development process. Knowing this mistake will prevent you from making it and leveraging the mental models in software application development. Some common errors are as follows:

• Many software professionals consider mental models inflexible and unchangeable. This mistake often leads to depending on outdated or unaligned mental models for specific software projects. Due to this, they may need better decisions.
• Some software professionals implement mental models in the development process too broadly. They assume that what solution works for one application or scenario will work for another or similar application. This leads to inaccurate conclusions.
• We know that the mental model helps simplify, but oversimplifying complex software applications may result in a non-consideration of all pertinent factors affecting functionality.
• People often look for information that supports what they already believe while ignoring evidence against it. It is called confirmation bias. It can result in having a one-sided or biased view of a situation and make it difficult to make good decisions.

Overcoming Challenges in Applying Mental Models

Here are some standard solutions for effectively using mental models in software development:

• It is suggested that the mental models must be continuously updated based on new experiences, learning skills, and expertise.
• Consistently collaborate with different teams and software professionals with other skills and backgrounds to integrate their views to have more adaptable mental models.
• Encourage seeking feedback from the team on implementing a mental model. This will help you identify any errors and give a reliable solution.
• You should collect data and assess whether the outcomes align with the mental model's predictions.
• It is advised to promote data-driven decision-making in conjunction with mental models.

Conclusion

Understanding the appropriate mental model framework for each circumstance allows the software professional to operate more intelligently rather than exerting excessive effort. Addressing different problems can require substantial time and energy.

Mental models help the team to divide complex issues into more manageable components, allowing them to understand the core and formulate pragmatic resolutions. Recognize that integrating these mental models into your daily routine might necessitate some time. Nevertheless, once you've mastered and implemented the procedure, you can swiftly overcome obstacles and find your path in the correct direction.