The Evolution of APL, the HOPL I paper by Falkoff and Iverson on APL, recounted the fundamental design principles which shaped the implementation of the APL language in 1966, and the early uses and other influences which shaped its first decade of enhancements.
In the 40 years that have elapsed since HOPL I, several dozen APL implementations have come and gone. In the first decade or two, interpreters were typically born and buried along with the hardware or operating system that they were created for. More recently, the use of C as an implementation language provided APL interpreters with greater longevity and portability.
APL started its life on IBM mainframes which were time-shared by multiple users. As the demand for computing resources grew and costs dropped, APL first moved in-house to mainframes, then to mini- and micro-computers. Today, APL runs on PCs and tablets, Apples and Raspberry Pis, smartphones and watches.
The operating systems, and the software application platforms that APL runs on, have evolved beyond recognition. Tools like database systems have taken over many of the tasks that were initially implemented in APL or provided by the APL system, and new capabilities like parallel hardware have also changed the focus of design and implementation efforts through the years.
The first wave of significant language enhancements occurred shortly after HOPL I, resulting in so-called second-generation APL systems. The most important feature of the second generation is the addition of general arrays—in which any item of an array can be another array—and a number of new functions and operators aligned with, if not always motivated by, the new data structures.
The majority of implementations followed IBM’s path with APL2 “floating” arrays; others aligned themselves with SHARP APL and “grounded” arrays. While the APL2 style of APL interpreters came to dominate the mainstream of the APL community, two new cousins of APL descended from the SHARP APL family tree: J (created by Iverson and Hui) and k (created by Arthur Whitney).
We attempt to follow a reasonable number of threads through the last 40 years, to identify the most important factors that have shaped the evolution of APL. We will discuss the details of what we believe are the most significant language features that made it through the occasionally unnatural selection imposed by the loss of habitats that disappeared with hardware, software platforms, and business models.
The history of APL now spans six decades. It is still the case, as Falkoff and Iverson remarked at the end of the HOPL I paper, that:
<blockquote>Although this is not the place to discuss the future, it should be remarked that the evolution of APL is far from finished.</blockquote>
By 2006, C++ had been in widespread industrial use for 20 years. It contained parts that had survived unchanged since introduced into C in the early 1970s as well as features that were novel in the early 2000s. From 2006 to 2020, the C++ developer community grew from about 3 million to about 4.5 million. It was a period where new programming models emerged, hardware architectures evolved, new application domains gained massive importance, and quite a few well-financed and professionally marketed languages fought for dominance. How did C++ -- an older language without serious commercial backing -- manage to thrive in the face of all that?
This paper focuses on the major changes to the ISO C++ standard for the 2011, 2014, 2017, and 2020 revisions. The standard library is about 3/4 of the C++20 standard, but this paper's primary focus is on language features and the programming techniques they support.
The paper contains long lists of features documenting the growth of C++. Significant technical points are discussed and illustrated with short code fragments. In addition, it presents some failed proposals and the discussions that led to their failure. It offers a perspective on the bewildering flow of facts and features across the years. The emphasis is on the ideas, people, and processes that shaped the language.
Themes include efforts to preserve the essence of C++ through evolutionary changes, to simplify its use, to improve support for generic programming, to better support compile-time programming, to extend support for concurrency and parallel programming, and to maintain stable support for decades' old code.
The ISO C++ standard evolves through a consensus process. Inevitably, there is competition among proposals and clashes (usually polite ones) over direction, design philosophies, and principles. The committee is now larger and more active than ever, with as many as 250 people turning up to week-long meetings three times a year and many more taking part electronically. We try (not always successfully) to mitigate the effects of design by committee, bureaucratic paralysis, and excessive enthusiasm for a variety of language fashions.
Specific language-technical topics include the memory model, concurrency and parallelism, compile-time computation, move-semantics, exceptions, lambda expressions, and modules. Designing a mechanism for specifying a template's requirements on its arguments that is sufficiently flexible and precise yet doesn't impose run-time costs turned out to be hard. The repeated attempts to design ``concepts'' to do that have their roots back in the 1980s and touch upon many key design issues for C++ and for generic programming.
The description is based on personal participation in the key events and design decisions, backed by the thousands of papers and hundreds of meeting minutes in the ISO C++ standards committee's archives.
Clojure was designed to be a general-purpose, practical functional language, suitable for use by professionals wherever its host language, e.g., Java, would be. Initially designed in 2005 and released in 2007, Clojure is a dialect of Lisp, but is not a direct descendant of any prior Lisp. It complements programming with pure functions of immutable data with concurrency-safe state management constructs that support writing correct multithreaded programs without the complexity of mutex locks.
Clojure is intentionally hosted, in that it compiles to and runs on the runtime of another language, such as the JVM. This is more than an implementation strategy; numerous features ensure that programs written in Clojure can leverage and interoperate with the libraries of the host language directly and efficiently.
In spite of combining two (at the time) rather unpopular ideas, functional programming and Lisp, Clojure has since seen adoption in industries as diverse as finance, climate science, retail, databases, analytics, publishing, healthcare, advertising and genomics, and by consultancies and startups worldwide, much to the career-altering surprise of its author.
Most of the ideas in Clojure were not novel, but their combination puts Clojure in a unique spot in language design (functional, hosted, Lisp). This paper recounts the motivation behind the initial development of Clojure and the rationale for various design decisions and language constructs. It then covers its evolution subsequent to release and adoption.
The coarray programming model is an expression of the Single-Program-Multiple-Data (SPMD) programming model through the simple device of adding a codimension to the Fortran language. A data object declared with a codimension is a coarray object. Codimensions express the idea that some objects are located in local memory while others are located in remote memory. Coarray syntax obeys most of the same rules for normal array syntax. It is familiar to the Fortran programmer so the use of coarray syntax is natural and intuitive. Although the basic idea is quite simple, inserting it into the language definition turned out to be difficult.
In addition, the process was complicated by rapidly changing hardware and heated arguments over whether parallelism should be supported best as an interface to language-independent libraries, as a set of directives superimposed on languages, or as a set of specific extensions to existing languages.
In this paper, we review both the early history of coarrays and also their development into a part of Fortran 2008 and eventually into a larger part of Fortran 2018. Coarrays have been used, for example, in weather forecasting and in neural networks and deep learning.
As its name suggests, the initial motivation for the D programming language was to improve on C and C++ while keeping their spirit. The D language was to preserve those languages' efficiency, low-level access, and Algol-style syntax. The areas D set out to improve focused initially on rapid development, convenience, and simplifying the syntax without hampering expressiveness.
The genesis of D has its peculiarities, as is the case with many other languages. Walter Bright, D's creator, is a mechanical engineer by education who started out working for Boeing designing gearboxes for the 757. He was programming games on the side, and in trying to make his game Empire run faster, became interested in compilers. Despite having no experience, Bright set out in 1982 to implement a compiler that produced better code than those on the market at the time.
Between 1999 and 2006, Bright worked alone on the D language definition and its implementation, although a steadily increasing volume of patches from users was incorporated. The new language would be based on the past successes of the languages he'd used and implemented, but would be clearly looking to the future. D started with choices that are obvious today but were less clear winners back in the 1990s: full support for Unicode, IEEE floating point, 2s complement arithmetic, and flat memory addressing (memory is treated as a linear address space with no segmentation). It would do away with certain compromises from past languages imposed by shortages of memory (for example, forward declarations would not be required). It would primarily appeal to C and C++ users, as expertise with those languages would be readily transferrable. The interface with C was designed to be zero cost.
The language design was begun in late 1999. An alpha version appeared in 2001 and the initial language was completed, somewhat arbitrarily, at version 1.0 in January 2007. During that time, the language evolved considerably, both in capability and in the accretion of a substantial worldwide community that became increasingly involved with contributing. The front end was open-sourced in April 2002, and the back end was donated by Symantec to the open source community in 2017. Meanwhile, two additional open-source back ends became mature in the 2010s: `gdc` (using the same back end as the GNU C++ compiler) and `ldc` (using the LLVM back end).
The increasing use of the D language in the 2010s created an impetus for formalization and development management. To that end, the D Language Foundation was created in September 2015 as a nonprofit corporation overseeing work on D's definition and implementation, publications, conferences, and collaborations with universities.
While Emacs proponents largely agree that it is the world’s greatest text editor, it is almost as much a Lisp machine disguised as an editor. Indeed, one of its chief appeals is that it is programmable via its own programming language. Emacs Lisp is a Lisp in the classic tradition. In this article, we present the history of this language over its more than 30 years of evolution. Its core has remained remarkably stable since its inception in 1985, in large part to preserve compatibility with the many third-party packages providing a multitude of extensions. Still, Emacs Lisp has evolved and continues to do so.
Important aspects of Emacs Lisp have been shaped by concrete requirements of the editor it supports as well as implementation constraints. These requirements led to the choice of a Lisp dialect as Emacs’s language in the first place, specifically its simplicity and dynamic nature: Loading additional Emacs packages or changing the ones in place occurs frequently, and having to restart the editor in order to re-compile or re-link the code would be unacceptable. Fulfilling this requirement in a more static language would have been difficult at best.
One of Lisp’s chief characteristics is its malleability through its uniform syntax and the use of macros. This has allowed the language to evolve much more rapidly and substantively than the evolution of its core would suggest, by letting Emacs packages provide new surface syntax alongside new functions. In particular, Emacs Lisp can be customized to look much like Common Lisp, and additional packages provide multiple-dispatch object systems, legible regular expressions, programmable pattern-matching constructs, generalized variables, and more. Still, the core has also evolved, albeit slowly. Most notably, it acquired support for lexical scoping.
The timeline of Emacs Lisp development is closely tied to the projects and people who have shaped it over the years: We document Emacs Lisp history through its predecessors, Mocklisp and MacLisp, its early development up to the “Emacs schism” and the fork of Lucid Emacs, the development of XEmacs, and the subsequent rennaissance of Emacs development.
This paper describes the genesis and early history of the F# programming language. I start with the origins of strongly-typed functional programming (FP) in the 1970s, 80s and 90s. During the same period, Microsoft was founded and grew to dominate the software industry. In 1997, as a response to Java, Microsoft initiated internal projects which eventually became the .NET programming framework and the C# language. From 1997 the worlds of academic functional programming and industry combined at Microsoft Research, Cambridge. The researchers engaged with the company through Project 7, the initial effort to bring multiple languages to .NET, leading to the initiation of .NET Generics in 1998 and F# in 2002. F# was one of several responses by advocates of strongly-typed functional programming to the "object-oriented tidal wave" of the mid-1990s. The development of the core features of F# 1.0 happened from 2004-2007, and I describe the decision-making process that led to the "productization" of F# by Microsoft in 2007-10 and the release of F# 2.0. The origins of F#'s characteristic features are covered: object programming, quotations, statically resolved type parameters, active patterns, computation expressions, async, units-of-measure and type providers. I describe key developments in F# since 2010, including F# 3.0-4.5, and its evolution as an open source, cross-platform language with multiple delivery channels. I conclude by examining some uses of F# and the influence F# has had on other languages so far.
This paper describes the history of the Groovy programming language. At the time of Groovy’s inception, Java was a dominant programming language with a wealth of useful libraries. Despite this, it was perceived by some to be evolving slowing and to have shortcomings for scripting, rapid prototyping and when trying to write minimalistic code. Other languages seemed to be innovating faster than Java and, while overcoming some of Java’s shortcomings, used syntax that was less familiar to Java developers. Integration with Java libraries was also non-optimal.
Groovy was created as a complementary language to Java—its dynamic counterpart. It would look and feel like Java but focus on extensibility and rapid innovation. Groovy would borrow ideas from dynamic languages like Ruby, Python and Smalltalk where needed to provide compelling JVM solutions for some of Java’s shortcomings.
Groovy supported innovation through its runtime and compile-time metaprogramming capabilities. It supported simple operator overloading, had a flexible grammar and was extensible. These characteristics made it suitable for growing the language to have new commands (verbs) and properties (nouns) specific to a particular domain, a so called Domain Specific Language (DSL). While still intrinsically linked with Java, over time Groovy has evolved from a niche dynamic scripting language into a compelling mainstream language.
After many years as a principally dynamically-typed language, a static nature was added to Groovy. Code could be statically type checked or when dynamic features weren’t needed, they could be turned off entirely for Java-like performance. A number of nuances to the static nature came about to support the style of coding used by Groovy developers.
Many choices made by Groovy in its design, later appeared in other languages (Swift, C#, Kotlin, Ceylon, PHP, Ruby, Coffeescript, Scala, Frege, TypeScript and Java itself). This includes Groovy’s dangling closure, Groovy builders, null-safe navigation, the Elvis operator, ranges, the spaceship operator, and flow typing. For most languages, we don’t know to what extent Groovy played a part in their choices. We do know that Kotlin took inspiration from Groovy’s dangling closures, builder concept, default it parameter for closures, templates and interpolated strings, null-safe navigation and the Elvis operator.
The leadership, governance and sponsorship arrangements of Groovy have evolved over time, but Groovy has always been a successful highly collaborative open source project driven more by the needs of the community than by a vision of a particular company or person.
LabVIEW™ is unusual among programming languages in that we did not intend to create a new language but rather to develop a tool for non-programmer scientists and engineers to assist them in automating their test and measurement systems.
Prior experience creating software for controlling instruments led us to the perspective that the software ought to be modeled as a hierarchy of ”virtual instruments”. The lowest level virtual instruments were simply reflections of the individual physical instruments they controlled. Higher level virtual instruments combined lower level ones to deliver more complex measurements. A frequency response virtual instrument could be implemented using a voltmeter and a sine-wave generator inside a loop that stepped through a frequency range. This was mostly an abstract concept at the time because it was hard to imagine how an existing language or tool could provide the rich yet intuitive experience of using a real instrument.
Inspired by the first Macintosh computer, we quickly realized the graphical user interface would be a natural way to interact with a virtual instrument, but it also sparked our imaginations about using graphics for creating software at a higher level of abstraction.
The February 1982 issue of IEEE Computer was devoted to data-flow models of computation, and it convinced us that graphical data-flow diagrams needed to be part of the solution. The major difficulty we saw, however, was the need to use cycles in the data-flow diagram to represent loops. Cycles increased complexity and made diagrams hard to understand and even harder to create.
This concern led to a major innovation in creating LabVIEW: merging structured programming concepts with data-flow. We represented control-flow structures as boxes in a data-flow diagram. We knew how to reason about loops, so we could introduce them as first class elements of the graphical representation rather than being constructed from lower-level elements. A box could encapsulate the semantics of the iterative behavior; it could clearly separate the body of the loop (the diagram inside the box) from the code before and after the loop (the diagram outside the box); and, its boundary could hold iteration state information.
Those fundamental concepts of ”graphical”, ”structured” and ”data-flow” enabled us to propose a software product. We staffed up a small skunkworks team to implement it. We called it LabVIEW. It was to be an engineer’s tool for automating measurement systems. At first, we were reluctant to admit that we had created a graphical programming language. When we finally did, we nicknamed it G, for Graphical language, so we could talk about the language as distinct from the integrated development environment (IDE), LabVIEW. In practice, almost everyone refers to both the language and the IDE as LabVIEW.
Without intending to do so, we created a programming language radically different from those that came before, pioneering techniques of graphically creating and viewing code, eliminating manual memory management without adding garbage collection overhead, and anticipating the massively parallel systems of the modern era. LabVIEW continues to evolve and thrive after more than 30 years.
Logo is more than a programming language. It is a learning environment where children explore mathematical ideas and create projects of their own design. Logo, the first computer language explicitly designed for children, was invented by Seymour Papert, Wallace Feurzeig, Daniel Bobrow, and Cynthia Solomon in 1966 at Bolt, Beranek and Newman, Inc. (BBN).
Logo’s design drew upon two theoretical frameworks: Jean Piaget’s constructivism and Marvin Minsky’s artificial intelligence research at MIT. One of Logo’s foundational ideas was that children should have a powerful programming environment. Early Lisp served as a model with its symbolic computation, recursive functions, operations on linked lists, and dynamic scoping of variables.
Logo became a symbol for change in elementary mathematics education and in the nature of school itself. The search for harnessing the computer’s potential to provide new ways of teaching and learning became a central focus and guiding principle in the Logo language development as it encompassed a widening scope that included natural language, music, graphics, animation, story telling, turtle geometry, robots, and other physical devices.
The fully parenthesized Cambridge Polish syntax of Lisp, originally regarded as a temporary expedient to be replaced by more conventional syntax, possesses a peculiar virtue: A read procedure can parse it without knowing the syntax of any expressions, statements, definitions, or declarations it may represent. The result of that parsing is a list structure that establishes a standard representation for uninterpreted abstract syntax trees.
This representation provides a convenient basis for macro processing, which allows the programmer to specify that some simple piece of abstract syntax should be replaced by some other, more complex piece of abstract syntax. As is well-known, this yields an abstraction mechanism that does things that procedural abstraction cannot, such as introducing new binding structures.
The existence of that standard representation for uninterpreted abstract syntax trees soon led Lisp to a greater reliance upon macros than was common in other high-level languages. The importance of those features is suggested by the ten pages devoted to macros in an earlier ACM HOPL paper, “The Evolution of Lisp.”
However, na'ive macro expansion was a leaky abstraction, because the movement of a piece of syntax from one place to another might lead to the accidental rebinding of a program’s identifiers. Although this problem was recognized in the 1960s, it was 20 years before a reliable solution was discovered, and another 10 before a solution was discovered that was reliable, flexible, and efficient.
In this paper, we summarize that early history with greater focus on hygienic macros, and continue the story by describing the further development, adoption, and influence of hygienic and partially hygienic macro technology in Scheme. The interplay between the desire for standardization and the development of new algorithms is a major theme of that story.
We then survey the ways in which hygienic macro technology has been adapted into recent non-parenthetical languages. Finally, we provide a short history of attempts to provide a formal account of macro processing.
The first MATLAB (the name is short for “Matrix Laboratory”) was not a programming language. Written in Fortran in the late 1970s, it was a simple interactive matrix calculator built on top of about a dozen subroutines from the LINPACK and EISPACK matrix software libraries. There were only 71 reserved words and built-in functions. It could be extended only by modifying the Fortran source code and recompiling it.
The programming language appeared in 1984 when MATLAB became a commercial product. The calculator was reimplemented in C and significantly enhanced with the addition of user functions, toolboxes, and graphics. It was available initially on the IBM PC and clones; versions for Unix workstations and the Apple Macintosh soon followed.
In addition to the matrix functions from the calculator, the 1984 MATLAB included fast Fourier transforms (FFT). The Control System Toolbox appeared in 1985 and the Signal Processing Toolbox in 1987. Built-in support for the numerical solution of ordinary differential equations also appeared in 1987.
The first significant new data structure, the sparse matrix, was introduced in 1992. The Image Processing Toolbox and the Symbolic Math Toolbox were both introduced in 1993.
Several new data types and data structures, including single precision floating point, various integer and logical types, cell arrays, structures, and objects were introduced in the late 1990s.
Enhancements to the MATLAB computing environment have dominated development in recent years. Included are extensions to the desktop, major enhancements to the object and graphics systems, support for parallel computing and GPUs, and the “Live Editor”, which combines programs, descriptive text, output and graphics into a single interactive, formatted document.
Today there are over 60 Toolboxes, many programmed in the MATLAB language, providing extended capabilities in specialized technical fields.
The roots of Objective-C began at ITT in the early 1980s in a research group led by Tom Love investigating improving programmer productivity by an order of magnitude, a concern motivated by the perceived "software crisis" articulated in the late 1960s. In 1981, Brad Cox, a member of this group, began to investigate Smalltalk and object-oriented programming for this purpose, but needed a language compatible with the Unix and C environments used by ITT. That year, Cox quickly wrote up the Object-Oriented Pre-Compiler (OOPC) that would translate a Smalltalk-like syntax into C.
Love felt there was a market for object-oriented solutions that could coexist with legacy languages and platforms, and after a brief stint at Schlumberger-Doll, co-founded with Cox Productivity Products International (PPI), later renamed as Stepstone, to pursue this. At PPI, Cox developed OOPC into Objective-C. Cox saw Objective-C as a crucial link in his larger vision of creating a market for "pre-fabricated" software components ("software-ICs"), which could be bought off the shelf and which, Cox believed, would unleash a "software industrial revolution."
Steve Naroff joined Stepstone in 1986 as Steve Jobs' NeXT Computer became an important customer for Objective-C, as it was being used in its NeXTSTEP operating system. Naroff became the primary Stepstone developer addressing NeXT's issues with Objective-C, solving a key fragility problem preventing NeXT from deploying forwards-compatible object libraries. Impressed with NeXT, Naroff left Stepstone for NeXT in 1988, and once there, added Objective-C support to Richard Stallman's GNU GCC compiler, which NeXT was using as its C compiler, removing the need to use Stepstone's ObjC to C translator. Over the next several years, Naroff and others would add significant new features to Objective-C, such as "categories," "protocols," and the ability to mix in C++ code. When Stepstone folded in 1994, all rights to Objective-C were acquired by NeXT. This eventually transferred to Apple when NeXT was acquired by Apple in 1997. Objective-C became the basis for Apple's Mac OS X and then iOS platforms, and Naroff and others at Apple added additional features to the language in the late 2000s as the iPhone App Store greatly expanded Objective-C's user base.
Oz is a programming language designed to support multiple programming paradigms in a clean factored way that is easy to program despite its broad coverage. It started in 1991 as a collaborative effort by the DFKI (Germany) and SICS (Sweden) and led to an influential system, Mozart, that was released in 1999 and widely used in the 2000s for practical applications and education. We give the history of Oz as it developed from its origins in logic programming, starting with Prolog, followed by concurrent logic programming and constraint logic programming, and leading to its two direct precursors, the concurrent constraint model and the Andorra Kernel Language (AKL). We give the lessons learned from the Oz effort including successes and failures and we explain the principles underlying the Oz design. Oz is defined through a kernel language, which is a formal model similar to a foundational calculus, but that is designed to be directly useful to the programmer. The kernel language is organized in a layered structure, which makes it straightforward to write programs that use different paradigms in different parts. Oz is a key enabler for the book Concepts, Techniques, and Models of Computer Programming (MIT Press, 2004). Based on the book and the implementation, Oz has been used successfully in university-level programming courses starting from 2001 to the present day.
Data science is increasingly important and challenging. It requires computational tools and programming environments that handle big data and difficult computations, while supporting creative, high-quality analysis. The R language and related software play a major role in computing for data science. R is featured in most programs for training in the field. R packages provide tools for a wide range of purposes and users. The description of a new technique, particularly from research in statistics, is frequently accompanied by an R package, greatly increasing the usefulness of the description.
The history of R makes clear its connection to data science. R was consciously designed to replicate in open-source software the contents of the S software. S in turn was written by data analysis researchers at Bell Labs as part of the computing environment for research in data analysis and collaborations to apply that research, rather than as a separate project to create a programming language. The features of S and the design decisions made for it need to be understood in this broader context of supporting effective data analysis (which would now be called data science). These characteristics were all transferred to R and remain central to its effectiveness. Thus, R can be viewed as based historically on a domain-specific language for the domain of data science.
This paper presents a personal view of the evolution of six generations of Smalltalk in which the author played a part, starting with Smalltalk-72 and progressing through Smalltalk-80 to Squeak and Etoys. It describes the forces that brought each generation into existence, the technical innovations that characterized it, and the growth in understanding of object-orientation and personal computing that emerged. It summarizes what that generation achieved and how it affected the future, both within the evolving group of developers and users, and in the outside world.
The early Smalltalks were not widely accessible because they ran only on proprietary Xerox hardware; because of this, few people have experience with these important historical artifacts. To make them accessible, the paper provides links to live simulations that can be run in present-day web browsers. These simulations offer the ability to run pre-defined scripts, but also allow the user to go off-script, browse the details of the implementation, and try anything that could be done in the original system. An appendix includes anecdotal and technical aspects of how examples of each generation of Smalltalk were recovered, and how order was teased out of chaos to the point that these old systems could be brought back to life.
The ML family of strict functional languages, which includes F#, OCaml, and Standard ML, evolved from the Meta Language of the LCF theorem proving system developed by Robin Milner and his research group at the University of Edinburgh in the 1970s. This paper focuses on the history of Standard ML, which plays a central role in this family of languages, as it was the first to include the complete set of features that we now associate with the name “ML” (i.e., polymorphic type inference, datatypes with pattern matching, modules, exceptions, and mutable state).
Standard ML, and the ML family of languages, have had enormous influence on the world of programming language design and theory. ML is the foremost exemplar of a functional programming language with strict evaluation (call-by-value) and static typing. The use of parametric polymorphism in its type system, together with the automatic inference of such types, has influenced a wide variety of modern languages (where polymorphism is often referred to as generics). It has popularized the idea of datatypes with associated case analysis by pattern matching. The module system of Standard ML extends the notion of type-level parameterization to large-scale programming with the notion of parametric modules, or functors.
Standard ML also set a precedent by being a language whose design included a formal definition with an associated metatheory of mathematical proofs (such as soundness of the type system). A formal definition was one of the explicit goals from the beginning of the project. While some previous languages had rigorous definitions, these definitions were not integral to the design process, and the formal part was limited to the language syntax and possibly dynamic semantics or static semantics, but not both.
The paper covers the early history of ML, the subsequent efforts to define a standard ML language, and the development of its major features and its formal definition. We also review the impact that the language had on programming-language research.
This paper describes the history of the Verilog hardware description language (HDL), including its influential predecessors and successors. Since its creation in 1984 and first sale in 1985, Verilog has completely revolutionized the design of hardware. Verilog enabled the development and wide acceptance of logic synthesis. For large-scale digital logic design, previous schematic-based techniques have transformed into textual register-transfer level (RTL) descriptions written in Verilog. As of 2018 about 80% of integrated circuit design teams worldwide use Verilog and its compatible descendant SystemVerilog.