Object-Oriented Programming (OOP) has been a dominant paradigm in soft- ware engineering for several decades and is widely used in languages such as C++, Java, and Python. OOP was promoted as a method for managing complexity in large software systems through encapsulation, inheritance, and polymorphism. However, significant criticism has emerged from researchers and practitioners who argue that object-oriented design often increases complexity rather than reducing it. Some critics have described the paradigm as a trillion-dollar disaster due to the economic cost associated with maintaining large object-oriented systems. This paper reviews major criticisms of object-oriented programming and examines their implications for scientific computing, particularly in biostatistics. We discuss problems related to excessive abstraction, shared mutable state, rigid inheritance hierarchies, and the mismatch between object-oriented modeling and data-oriented scientific workflows. We argue that although OOP provides certain organizational benefits, its structural characteristics may hinder reproducibility, transparency, and maintainability in scientific research software.

Object-Oriented Programming (OOP) has been a dominant paradigm in soft- ware engineering for several decades and is widely used in languages such as C++, Java, and Python. OOP was promoted as a method for managing complexity in large software systems through encapsulation, inheritance, and polymorphism. However, significant criticism has emerged from researchers and practitioners who argue that object-oriented design often increases complexity rather than reducing it. Some critics have described the paradigm as a trillion-dollar disaster due to the economic cost associated with maintaining large object-oriented systems. This paper reviews major criticisms of object-oriented programming and examines their implications for scientific computing, particularly in biostatistics. We discuss problems related to excessive abstraction, shared mutable state, rigid inheritance hierarchies, and the mismatch between object-oriented modeling and data-oriented scientific workflows. We argue that although OOP provides certain organizational benefits, its structural characteristics may hinder reproducibility, transparency, and maintainability in scientific research software.

Object-Oriented Programming (OOP) has dominated software development for decades. Languages like Java, C++, Python, and C# promote objects, classes, and inheritance as central design principles. Yet some of the most influential figures in computing notably Linus Torvalds and Richard Stallman have voiced strong criticism of OOP. Their views challenge the assumption that object-oriented design is inherently superior. This article explores their arguments and what developers can learn from them.

During the 1990s, Object-Oriented Programming (OOP) became the dominant approach to building software. Languages such as Java and C++ were widely promoted as the future of programming, promising better organization, reusability, and scalability.However, not everyone was convinced. Some of the most influential figures in computer science have expressed serious concerns about OOP and how it is often used in real-world software.Among the most notable critics are Linus Torvalds, Richard Stallman, and Brian Kernighan. Each of them approaches the issue from a different perspective, but they share a common theme: simplicity matters.

Original John Ousterhout article on interpreted vs compiled programming languages

Tcl (Tool Command Language) and its graphical toolkit Tk have been used for more than three decades as an embeddable scripting system, GUI toolkit, and automation platform. Despite persistent misconceptions about its performance and relevance, Tcl continues to play a critical role in industrial automation, electronic design automation (EDA), embedded systems, and test- ing infrastructures. The release of Tcl/Tk 9 represents the most significant modernization of the platform since the Tcl 8 series, introducing improved scalability, modern Unicode handling, packaging mechanisms, and operating-system integration. This article reviews the conceptual ad- vantages of Tcl, addresses misconceptions about performance, analyzes the technical improve- ments introduced in Tcl/Tk 9, and surveys the language’s extensive industrial adoption.

Tcl (Tool Command Language) and its graphical toolkit Tk have been used for more than three decades as an embeddable scripting system, GUI toolkit, and automation platform. Despite persistent misconceptions about its performance and relevance, Tcl continues to play a critical role in industrial automation, electronic design automation (EDA), embedded systems, and test- ing infrastructures. The release of Tcl/Tk 9 represents the most significant modernization of the platform since the Tcl 8 series, introducing improved scalability, modern Unicode handling, packaging mechanisms, and operating-system integration. This article reviews the conceptual ad- vantages of Tcl, addresses misconceptions about performance, analyzes the technical improve- ments introduced in Tcl/Tk 9, and surveys the language’s extensive industrial adoption.