Computer-Aided Software Engineering

Computer-aided software engineering (CASE) refers to the use of computational tools to simplify the job of creating software systems. That might refer to the top-down design of systems, or to the bottom-up activity of actually writing code. When digital computers were first invented, they had to be programmed by setting manual switches. Since then, the entire history of computing has been about making that task easier. Assembly language made it easier to write programs compared to binary code. Compilers relieved software engineers for the need to write assembly language. Object-orientation facilitated code reuse and interoperability. Graphical development tools automated the tasks of debugging, testing, code control, etc. The introduction of tools specifically labelled as CASE tools could therefore be considered redundant, especially since a lot of the functionality of older CASE tools is now available in common IDEs. However, it serves to highlight some aspects of software engineering that are aimed at a slightly higher level of abstraction.

Early Adoption

Early CASE adoption

In the early 1980s, the increasing complexity of software and the need for more structured methodologies gave rise to the CASE concept which initially focused on automating manual tasks like drawing flowcharts or data flow diagrams. During this period, a notable distinction emerged between Upper CASE and Lower CASE tools. Upper CASE tools helped with initial system design and analysis, while Lower CASE tools aided in the actual construction and maintenance of systems. As the field evolved, some advanced CASE tools, like the Information Engineering Facility (IEF), introduced repository-based systems to manage the vast amount of information associated with large software projects. However, as different CASE tools emerged, a significant challenge arose around integrating their functionality and sharing data among them. For more details on this period, please see the Butler Cox Foundation Research Report 57 from 1988.

Growth, Integration and Standardisation

In the 1990s, integrated CASE (I-CASE) tools emerged as a comprehensive solution to integration challenges, providing both upper and lower CASE functionalities within a unified system. Concurrently, the rise of object-oriented programming (OOP) spurred the popularity of object-oriented CASE (OO-CASE) tools. To improve how CASE tools managed and exchanged data, initiatives such as the CASE Data Interchange Format (CDIF) were launched, aiming for standardization. However, as the market became saturated with numerous tools, a period of consolidation ensued, during which larger software vendors acquired specialized CASE tool providers. For more details on this period, please refer to the Proceedings of the 7th International Workshop on CASE (1995).

Transition & Evolution

With the rising popularity of Agile methodologies and Extreme Programming (XP), the software development industry began shifting away from heavyweight, repository-based CASE tools towards more flexible and lightweight methods. Concurrently, the Unified Modeling Language (UML) emerged as the de facto standard for software modelling. This shift catalyzed a surge in the development and adoption of UML-based CASE tools, facilitating more dynamic and iterative design processes. Additionally, the Object Management Group (OMG) introduced the Model-Driven Architecture (MDA), which further advanced the concept of software development driven by models. This approach enabled the creation of tools capable of generating code directly from UML diagrams, streamlining the software development lifecycle.

Model-driven development (MDD) is a formalisation of the idea that given a precise enough model of a software system, the software itself can be generated through a deterministic process. MDD was a popular concept in the 2000s as various authors and tool vendors tried to use UML as the basis for generating code. The main idea is that working with a model using a graphical representation such as UML is quicker, easier and more intuitive than working directly with code. The approach was criticised in several ways (Hailpern & Tarr, 2006):

Redundancy: Multiple representations of the same software concepts are needed to capture the full picture leading to complexity and duplication.
Round-trip problems: Given the complex set of documents required for the realistic representation of a software system, keeping them all consistent becomes a major overhead.
Moving complexity rather than reducing it: To represent the full complexity of a software system would require an equally complex model. This point recalls the short story by Jorge Luis Borges which imagine a 1:1 scale map.
Additional skills requirements: With each representation format, the developer would be required to learn its rules and operation. Again, this could potentially represent additional overhead rather than a reduction in effort.

Other academics have (informally) argued that UML is not the appropriate medium for MDD since it models code and not the behaviour of the desired system. All of these criticisms, though, are theoretical. The test of a technical approach should be in whether it delivers useful results of not. This might be called the pragmatic criterion. Several tools exist that allow the user to create a set of UML diagrams and then to generate code from them. None of them will deliver a perfect working system; however, they all deliver well-structured code that reduces the amount of repetitive content that would otherwise be the responsibility of the software engineer. Some examples are:

For more information about this period, please refer to the overview by Sharon, (2003).

Modern Tools & DevOps Integration

The evolution of DevOps practices brought about significant advancements in Continuous Integration and Continuous Deployment (CI/CD), resulting in tools that seamlessly integrate with modern development, testing, and deployment pipelines. Concurrently, CASE tools adapted to the changing technological landscape by transitioning to cloud-based platforms, which enhanced collaboration among distributed teams. Moreover, modern Integrated Development Environments (IDEs) began to natively incorporate many CASE capabilities, often blurring the distinctions between CASE tools and IDEs, thereby streamlining the software development process. This integration and evolution paved the way for the rise of the low-code approach, an extension of model-driven development that allows developers to create applications with minimal hand-coding and lower technical complexity. Low-code platforms utilize visual programming interfaces enriched by the powerful capabilities of model-driven environments, significantly accelerating development cycles and democratizing application development. For a further discussion of low-code, please refer to Cabot (2020).

Generative AI

Generative AI is revolutionizing software engineering by enhancing various aspects of CASE tools. In the realm of code writing and assistance, generative AI contributes through automatic code generation, intelligent code completion, and efficient bug detection, streamlining the development process. It also plays a crucial role in improving documentation by automatically generating detailed documentation, enhancing inline comments, and translating complex technical jargon into more understandable language.

For code reviews, AI-driven tools automate the review process, offering explanations for code logic and improving code quality. These tools address requirements analysis by interpreting requirements and performing gap analysis, thus ensuring that the software developed meets the intended specifications. In software design and modeling, AI capabilities extend to generating UML diagrams and suggesting design patterns that optimize the architectural setup of software systems.

Testing and Quality Assurance (QA) benefit immensely from generative AI through the automatic generation of test cases and the interpretation of error messages, which enhances the efficiency of testing processes. The educational aspects of software development are also enriched by AI through interactive learning platforms and tools that respond to technical queries, facilitating a deeper understanding for developers.

Finally, in software maintenance, generative AI aids in understanding legacy code and suggesting code refactoring, ensuring that older systems stay up to date with modern standards and practices. Collectively, these advancements demonstrate how generative AI, as a CASE tool, is integral in modernizing the software engineering landscape, making it more efficient, understandable, and maintainable.

Practical tips on using CASE