Software development is a process of writing and maintaining the source code, but in a broader sense, it includes all that is involved between the conception of the desired software through to the final manifestation of the software, sometimes in a planned and structured process.
There are over 300 different ("common") computer languages in existence, apart from the various dialects stemming from one of them. Most of them can be classified in definable groups, but others don’t belong to anything. Some because they are rather new or the use of them was or is never wide spread and only used by a small specialized professionals or groups of scientists requiring these dialects. This is often the case with a specific language that was designed for just one purpose, e.g. telecommunication or supercomputing.
Some languages are even dead languages, some others are revived and expanded upon again, and there are ones that constantly rejuvenate. In the latter case a programmer is sometimes wondering whether he or she is not just upgrading to a newer version but instead learning a complete new language.
The software can be developed for a variety of purposes, the three most common being to meet specific needs of a specific client/business, to meet a perceived need of some set of potential users (the case with commercial and open source software), or for personal use.
Computer scientist Tom Kilburn is responsible for writing the world’s very first piece of software, which was run at 11 a.m. on June 21, 1948, at the University of Manchester in England. Kilburn and his colleague Freddie Williams had built one of the earliest computers, the Manchester Small-Scale Experimental Machine (also known as the “Baby”). The SSEM was programmed to perform mathematical calculations using machine code instructions. It was written by Tom Kilburn, and calculated the highest factor of the integer 2^18 = 262,144.
For decades after this groundbreaking event, computers were programmed with punch cards in which holes denoted specific machine code instructions. Fortran, one of the very first higher-level programming languages, was originally published in 1957. The next year, statistician John Tukey coined the word “software” in an article about computer programming. Other pioneering programming languages like Cobol, BASIC, Pascal and C arrived over the next two decades.
Evolutionary model is a combination of Iterative and Incremental model of software development life cycle. ... The Evolutionary development model divides the development cycle into smaller, incremental waterfall models in which users are able to get access to the product at the end of each cycle.
The first true electronic computer was the ENIAC (Electronic Numerator, Integrator,Analyzer and Computer). In 1942 a 35 year old engineer named John W. Mauchly wrote amemo to the US government outlining his ideas for an “electronic computor” (McCartney, 1999: 49). His ideas were ignored at first, but they were soon taken up with alacrity, for they promised to solve one of the military’s most pressing problems.
That was the calculation of ballistics tables, which were needed in enormous quantities to help the artillery fire their weapons at the correct angles. The US government’s Ballistics Research Laboratory commissioned a project based on Mauchly’s proposal in June 1943. Mauchly led a team of engineers, including a young graduate student called J. Presper Eckert, in the construction of a general purpose computer that could solve any ballistics problem and provide the reams of tables demanded by the military.
The machine used vacuum tubes, a development inspired by Mauchly’s contacts with John Atanasoff, who used them as switches instead of mechanical relays in a device he had built in the early 1940s (Augarten, 1985: 114). Atanosoff’s machine, the ABC, was the first fully electronic calculator. ENIAC differed significantly from all devices that went before it. It was programmable. Its use of stored memory and electronic components, and the decision to make it a general purpose device, mark it as the first true electronic computer.
But despite Mauchly and Eckert’s best efforts ENIAC, with 17,000 vacuum tubes and weighing over 30 tonnes, was not completed before the end of the war. It ran its first program in November 1945, and proved its worth almost immediately in running some of the first calculations in the development of the H-Bomb (a later version, appropriately named MANIAC, was used exclusively for that purpose).
By modern day standards, programming ENIAC was a nightmare. The task was performed by setting switches and knobs, which told different parts of the machine (known as “accumulators”) which mathematical function to perform. ENIAC operators had to plug accumulators together in the proper order, and preparing a program to run could take a month or more (McCartney, 1999: 90-94).
ENIAC led to EDVAC (Electronic Discrete Variable Computer), incorporating many of the ideas of John von Neumann, a well-known and respected mathematician who lent a significant amount of credibility to the project (Campbell-Kelly and Aspray, 1996:92).
Neumann also bought significant intellectual rigour to the team, and his famous paper “report on EDVAC” properly outlined for the first time exactly what an electronic computer was and how it should work. Von Neumann’s report defined five key components to a computer – input and output, memory, and a control unit and arithmetical unit. We still refer to the “Von Neumann architecture” of today’s computers.
When the war was over, Mauchly and Eckert decided to commercialise their invention. They developed a machine called the UNIVAC (Universal Automatic Computer), designed for general purpose business use. But they were better engineers than they were businessmen, and after many false starts their small company was bought by office machine giant Remington Rand in 1950. The first commercial machine was installed in the US Census Bureau.
UNIVAC leapt to the forefront of public consciousness in the 1952 US presidential election, where it correctly predicted the results of the election based on just one hour’s counting. It was not a particularly impressive machine by today’s standards (it still used decimal arithmetic, for a start), but nearly 50 of the original model were sold.
The 1950s was a decade of significant improvements in computing technology. The efforts of Alan Turing and his Bletchley Park codebreakers during World War II led to a burgeoning British computer industry. Before his death, after studying von Neumann’s EDVAC paper, Turing designed the ACE (Automatic Computing Engine), which led to the Manchester Mark I, technically a far superior machine to ENIAC or EDVAC (Augarten, 1984: 148). It was commercialised by Ferranti, one of the companies that was later to merge to form ICL, the flag bearer of the British computer industry.
The most significant US developments of the 1950s were the Whirlwind and SAGE projects. MIT’s Whirlwind was smaller than ENIAC, but it introduced the concepts of real-time computing and magnetic core memory. It was built by a team lead by Ken Olsen, who later founded Digital Equipment Corporation, the company that led the minicomputer revolution of the 1970s (Ceruzzi, 1999: 140).
SAGE (Semi-Automatic Ground Environment) was a real-time air defence system built for the US government in the Cold War. The project was accorded top priority, with a virtually unlimited budget. In a momentous decision, the government awarded the contract to a company that had only just decided to enter the computer industry. That company’s name was IBM.
SAGE broke new ground on a number of fronts. The first was its sheer size. There were 26 data centres, each with a 250 tonne SAGE mainframe. It was built from a number of modules that could be swapped in and out. It was the world’s first computer network, using the world’s first fault-tolerant computers and the world’s first graphical displays. And it gave IBM a head start in the computer industry that it has retained ever since (Augarten, 1985: 204).
By the end of the 1950s there were dozens of players in the computer industry. Remington Rand had become Sperry Rand, and others like RCA, Honeywell, General Electric, Control Data and Burroughs had entered the field. The UK saw the likes of Ferranti and International Computers and Singer, and continental Europe Bull and Siemens and Olivetti. In Japan, a 40 year old company called Fujitsu moved into computers.
All these machines, of course, ran software, but there was no software industry as we understand it today. Early commercial machines were programmed mechanically, or by the use of machine language. In the early days there was little understanding of the distinction between hardware and software. That was to change with the development of the first programming languages.
When starting with an effective software development plan, it is vital to lay out a detailed marketing strategy before anything else. Consider the following;
1. Who? Hiring developers is an important decision to scale up your company & fulfill your IT requirements. To make sure that you don’t waste time and effort pitching the wrong audience, go for market analysis, and in-depth competitor research.
Understand what kind of products are they seeing, why people are liking them, what marketing strategies are they following, and what is the USP of those products or services.
2. What? Once you have successfully determined the target audience, the second step is to decide what your business is going to do differently. How are you going to raise awareness among the audience and how will it manage to interest them?
This can be easily done by looking at the successful marketing plans from the competitors. In addition to this, you can come up with a completely new strategy of your own. It is advisable to choose an offshore software development company so that you can focus on your core competencies.
You just have to invest your little time in managing your remote team to ensure that everything is running smoothly.
3. Why? This factor in terms of creating the software development strategy or approach refers to why the software created by your company is worth the customer’s time and effort. Your investment will be wasted if the product you have created has no value.
If the product doesn’t do anything that customers are going to take note of, then what is the use? Ensure that the products that you are developing are something that people are desperately looking forward to.
In the coming decade, we will see even more innovation in the programming language landscape. Programming languages originated in the last decade will be even more popular, whereas many other new programming languages will hit the scene. In the 2030s, the programming language market share will be much more fragmented and even compared to today. Rust will replace C/C++ as the numero uno System programming language, whereas Julia will replace Python as the de facto language in AI. With AI-driven software development and innovative tools, Polyglot programming will be the norm rather than the exception in the 2030s.
By 2030s, WebAssembly will be the de facto Bytecode format to run on Web or Smart Devices with Multi-threading programming model support. It will allow writing Consumer applications (e.g., Web, Smart devices) with any languages taking the full advantages of underlined Hardware (e.g., GPU). As a result, powerful and near Metal language like Rust will be used to develop Gaming, 3-D, AR/VAR apps, or other CPU intensive apps targeting Web, Smart devices. Also, in the 2030s, the browser will be the Operating System, and almost 100% of consumer desktop applications will run on browsers.
Cloud Computing
In the next ten years, Cloud computing will be omnipresent in Software development. Also, the current issues of Cloud computing (e.g., Security) will be resolved in this decade. Google’s attempt to unify Cloud Stack via Cloud Native Foundation will gather more steam, and many Cloud Services will be standardized by 2030.
During the 2030s, Cloud Computing (Public/Private/Hybrid) will be the “normal” way of software development. Also, the on-prem data-center will use the “Standardized” Cloud stack or Vendor-specific Cloud Stack.
Due to the physical requirements of Quantum Computers, we will use Quantum Computing and Quantum Artificial Intelligence only in Cloud in the 2030s. By the late 2030s, Quantum Encryption will be mature and will give an unbreakable and robust security mechanism for Cloud computing. Whether we like it, Cloud computing in the 2030s will be centralized, and only the Big Tech companies will dominate it like today.
Artificial Intelligence
Artificial Intelligence is one of the earliest disciplines in Computer Science but faced several setbacks during the AI winters. In the next decade also, significant innovations and breakthroughs will happen in AI, especially in Reinforcement learning. AI will start eating the world in the 2030s. Contrary to popular belief, AI will aid humans instead of replacing humans. We will drive autonomous cars. The doctors will use AI for better treatment. Life Science companies will use AI for better drug development. Even as developers, we will use AI-driven Operating systems with AI-driven applications.
In the 2030s, AI will be wholly explainable and interpretable, unlike today. By then, AI will not only be able to find a Cat but also can explain or understand why it is a Cat. Breakthrough in Quantum Computing in the 2030s will significantly boost AI as Neural Network models will train on the fly and use the trained model instantly with the help of Quantum Computers. I expect we will see the AI Singularity i.e., it will continue to improve in a runaway fashion without human assistance.
Domain-Specific Hardware
Today’s Software applications are so vastly varied that there are many cases where specialized Hardware can give a significant advantage over generic Hardware. There are several examples of successful domain-specific Hardware development in the last few years. There is specialized Hardware for Bitcoin mining that can calculate the SHA more efficiently. Google has developed a specialized GPU (TPU) optimized for running TensorFlow. Another very successful specialized Hardware is Amazon AWS Nitro, which is a specialized Hardware for Containerization/Virtualization and helped Amazon significantly in their Serverless, EC2 platform.
In the next decade, we will see increasingly more specialized computing Hardware. In the 2030s, there will be a high number of specialized Hardware: special Hardware for Database, special Hardware for AI, specialized Hardware for Data Processing, and so on. Currently, Hardware development resembles the software development of pre-2010, which led to a long release cycle. In the 2030s, Hardware development will incorporate many best practices from Software Development. It will use Agile methodology with Cross-functional teams where Hardware Engineers will work together with Domain-specific Software Engineering. As a consequence, the Hardware release cycle will be shorter, which in turn will produce more domain-specific Hardware.
Distributed SQL
In the last few years, we have seen the rise of the Distributed SQL (NewSQL) databases, which combines the consistency of SQL with the scalability of the NoSQL database. Although many of them (Cockroach DB, AWS Aurora) are gaining lots of traction, there is room for improvement. In the next decade, we will see even more innovation in the Distributed SQL field.
In the 2030s, we can see real distributed SQL because of innovation in many other areas (e.g., specialized Hardware, quantum computing). One idea could be the “Quantum Entangled SQL Databases,” where clusters of quantum entangled SQL databases will offer the consistency of SQL databases even when one Database is on earth, and another database is in Mars.
Infrastructure: All roads lead to Cloud
2019 was a great year for Cloud vendors. Not only the startups are using the cloud but also conservative and security concern companies like Government Organizations, Health Care, Mining, Banks, Insurances and even Pentagon are moving towards the cloud.
Unified Data-Intensive Applications
In the last decade, we have seen an explosion of Data-Intensive applications. We have Batch Processing tools (Spark, Hadoop Map Reduce), Stream Processing tools (Flink, Strom), Queuing (Kafka, Pulsar), Full-text search tools (Solr, Elastic Search), Caching(Redis), Column Store (Cassandra), Row Store (SQL Databases). The downside is that there is no SQL like abstraction for data processing today. Currently, finding the right Data-Intensive tool for specific Data modeling is a daunting task.
In the next decade, we will see the convergence of many Data processing tools, which will offer unified Data modeling for both Batch and Stream processing. In the 2030s, we will find Data-Intensive applications less fragmented and more unified. We will also see tools that will abstract many data modeling (e.g., streaming, full-text searching, caching, column operations, row operations) in the same Data processing framework. Also, we will see the Data-Intensive applications more composable (like Unix) so that we can easily plug multiple applications
Blockchain
Blockchain is a disruptive technology, it has many limitations that are hindering its mass adoption. In the coming decade, we will see many innovations in Blockchain, and many of its limitations will be resolved. In the 2030s, Blockchain will be well-established Technology. It will be used in many fields, which are Contract/Transaction-based and centralized: Financial Transaction, Real Estate contracts, Oil and Gas purchase, Supply chain, copyright, sharing music. In the last decade, many advancements happened in Blockchain Technology and opened the door to use Blockchain in non-cryptocurrency use cases. During the 2030s, Quantum Computing will start to threaten classical Encryption. As conventional Encryption is key to Blockchain, it will go through significant changes during the 2030s and will adapt Quantum encryption.
There are different phases through which a software application goes through in the software development life cycle (SDLC). From designing to maintenance, a software application needs to be monitored at each phase. However, the complexity of a software application increases as the application builds on. The creation and development of an application are considered complex than its designing. The essence of successful software maintenance lies in the fact that how well the strategies are implemented. The strategies adopted during the software development life cycle improve and ease the software development task.
With time, the strategies keep on changing. Looking at the present software development industry, you can consider implementing the below-mentioned strategies.
Design Strategy
A system analyst or anyone who has the expertise in the niche is more proficient in the specification of the software. In order to accomplish the task of functional design, the software designing must be done by system designers. The system architect or designer makes use of primitive components to create a specification of a software artifact. Moreover, it involves both low-level component and algorithm design and high-level, architecture design.
Programming Strategy
Programming strategies must be made by experts. In addition, programmers must involve system designers to implement the software. This ensures that the functional and technical design goals are met. Software testing being an important element of the software quality assurance process requires you to perform it on a regular basis.
Maintenance Strategy
Maintenance strategy is a lifetime task and must be well written, as it might involve end-user for training. There are various kinds of software development models, with each one of them involving a strategy to perform a specific set of steps while the software is being developed.
A software development strategy is a set of upfront decisions that allows you to come up with an effective set of dos, don’ts and how’s regarding the future application design, development and deployment, and move consistently through each step of a development project.
Selecting a technology platform (it includes not only the language but also frameworks, patterns, APIs, and more) is important as the difference in the speed of development may be 2 – 20 times. At the same time, platforms can also differ in terms of reliability. We would recommend using a 4- quadrant matrix to sort the relevant platforms by these two attributes and choose the best match.
Apply automation
The introduction of automation in development, testing and deployment will foster the process too. Some of the key tools and processes of automation are:
Containerization. Containers are a lightweight alternative to virtual machines. They separate all software libraries and executable files, and, thus, streamline deployment and simplify management of large complex systems.
Test automation. Test automation means standardization of test planning, design, development and automation of test execution.
DevOps. The DevOps approach and its practices, like continuous integration and delivery (CI/CD), provide for more frequent and reliable software releases through aligning the development, testing and production environments.
Predicting the future is difficult. Predicting the future of Software Development is even more challenging and risky. The most important stacks of Software Development: from Cloud to the ByteCode. Technology is evolving faster than ever before. Business owners must be willing to adapt to changes in tech if they want to stay competitive. To do that, though, you must first keep yourself abreast of the latest trends.
Especially the amount of innovation and changes that happened in the last decade is unprecedented. Here are the significant changes we have witnessed in the last decade:
With rapid digitalization and Industry 4.0, we will see even more dramatic changes and innovations in the Software Development industry in the coming decade. In this article, here are some predictions about software development
Quantum Computing
Quantum computing has several challenges: the QPU needs to put in almost absolute Zero temperature, and Quantum computers produce huge errors due to quantum decoherence. In the next decade, Quantum Computing will be the hottest research topic as large corporations, and World Superpowers will vie for Quantum Supremacy.
In the early 2030s, Quantum Computing will start threatening the Classical cryptography and related fields like financial transactions, Blockchain. So, there will be massive changes and agitation in the industry as everyone will try to replace classical cryptography with Quantum Cryptography. By the late 2030s, Quantum Computing will finally break the classical cryptography, which may cause substantial social uproar like Wikileaks as it will decrypt many secure and sensitive communications.
Low Code/No Code
In the last few years, a fresh movement LCNC (Low-Code No-Code) is gaining traction, which is trying to reduce the barrier to product development. There exist many excellent LCNC applications that enable to develop the first product in a short time without any software engineers. Bubble, Huddle, Webflow offers speedy Web Application development. Kodika offers iOS app development with no Code. Parabola is a no Code Data Workflow platform, whereas Airtable is an LCNC database-spreadsheet hybrid. There is also an LCNC platform for AI/ML.
Current LCNC platforms need to go a long way to develop highly flexible, industry-grade applications. If we think about industry grade applications as Lego Mindstorms, then the current LCNC applications are like LEGO Duplo. In the next decade, LCNC platforms will evolve immensely. In the 2030s, there will be a plethora of mature LCNC platforms that can create industry grade applications. Entrepreneurs or Business executives will develop 80–90% of their consumer applications MVP using LCNC. There will be a few powerful AI-driven LCNC, which even Software Engineers will use to start a new App development. So if you have fresh ideas but have no money or no coding experience, the 2030s will be an excellent time for you.
Progressive Web Applications
Progressive web applications are the new thing and they are definitely gaining momentum at a fast speed. These are applications that find a way between websites and mobile applications. The problem with mobile applications is that they are difficult to develop and maintain. Not to mention, they are purely mobile applications and cannot take advantage of the web. On the other hand, progressive web applications combine the strengths of mobile applications and web in one place. When users use these applications, they feel like they are using a smart phone application. However, the web is also taking advantage of the web.
You have huge companies like Google that have started to get serious with PWAs. And when large tech companies start following a trend, the smaller ones have to follow suit naturally. The developers have to adjust to these changes as well. They have to understand that their skills are needed to develop PWAs just as much as they are needed for developing a mobile application.
The new decade is upon us and with it comes a flood of excitement for the changes and trends we will see in tech. Software development has become more integral to nearly every sector of the world, so developments and changes in software development have a vast impact on our lives. While we cannot always accurately predict what lies ahead for tech, there are some trends that we expect to continue into the new decade.
