API testing is also being widely used by software testers due to the difficulty of creating and maintaining GUI-based automation testing. It involves directly testing APIs as part of integration testing, to determine if they meet expectations for functionality, reliability, performance, and security.[10] Since APIs lack a GUI, API testing is performed at the message layer.[11] API testing is considered critical when an API serves as the primary interface to application logic since GUI tests can be difficult to maintain with the short release cycles and frequent changes commonly used with agile software development and DevOps.[12][13]
The picture is actually even worse than those numbers alone suggest, says Mark Muro, a senior fellow at the Brookings Institution. Existing federal “readjustment programs,” he says, include a collection of small initiatives—some dating back to the 1960s—addressing everything from military-­base closings to the needs of Appalachian coal-mining communities. But none are specifically designed to help people whose jobs have disappeared because of automation. Not only is the overall funding limited, he says, but the help is too piecemeal to take on a broad labor-force disruption like automation.
In general, testing is finding out how well something works. In terms of human beings, testing tells what level of knowledge or skill has been acquired. In computer hardware and software development, testing is used at key checkpoints in the overall process to determine whether objectives are being met. For example, in software development, product objectives are sometimes tested by product user representatives. When the design is complete, coding follows and the finished code is then tested at the unit or module level by each programmer; at the component level by the group of programmers involved; and at the system level when all components are combined together. At early or late stages, a product or service may also be tested for usability.

Automation is essential for many scientific and clinical applications.[78] Therefore, automation has been extensively employed in laboratories. From as early as 1980 fully automated laboratories have already been working.[79] However, automation has not become widespread in laboratories due to its high cost. This may change with the ability of integrating low-cost devices with standard laboratory equipment.[80][81] Autosamplers are common devices used in laboratory automation.

With the growing number of web-based applications this is changing, however, as verifying and testing web-based interfaces is easier and there are various tools that help with this, including free open source projects. Please see below for a list of popular and useful tools, projects, books and resources to get started with automated software testing.
“It felt weird to have free time during the day,” he told me. “I spent that time learning about the other systems in the hotel.” He then made himself useful, helping management with bottlenecks in those systems. Auto-automation had erased menial toil, reduced his stress, and let him pursue his actual interests. “In effect, I made my position into something I love, which is troubleshooting,” he says. Two weeks before he left, he handed his boss a diskette loaded with the program and documentation on how it ran. His boss was upset that he was quitting, Gary says—until he handed over the diskette, showed him how the program worked, and told him to call if there was ever any problem. No call ever came.
A search for the complementarities to which Autor was referring is at the heart of what we call an augmentation strategy. It stands in stark contrast to the automation strategies that efficiency-minded enterprises have pursued in the past. Automation starts with a baseline of what people do in a given job and subtracts from that. It deploys computers to chip away at the tasks humans perform as soon as those tasks can be codified. Aiming for increased automation promises cost savings but limits us to thinking within the parameters of work that is being accomplished today.

Information technology, together with industrial machinery and processes, can assist in the design, implementation, and monitoring of control systems. One example of an industrial control system is a programmable logic controller (PLC). PLCs are specialized hardened computers which are frequently used to synchronize the flow of inputs from (physical) sensors and events with the flow of outputs to actuators and events.[56]
Jennifer Thomé, Business Development and Marketing Manager at Plustek, believes, “The current state of business process automation is pretty abysmal for many companies, especially well-established ones that have to bring years of old processes and documents into the modern age. Doctors, accountants, and many government agencies are slowed down by the fact that they don't have the resources to update their systems and complete their work simultaneously.

RPA isn’t for every enterprise. As with any automation technology, RPA has the potential to eliminate jobs, which presents CIOs with challenges managing talent. While enterprises embracing RPA are attempting to transition many workers to new jobs, Forrester Research estimates that RPA software will threaten the livelihood of 230 million or more knowledge workers, or approximately 9 percent of the global workforce. 
Despite the excitement around AI, it is still in its early days. Driverless vehicles are fine on sunny days but struggle in the fog or the snow, and they still can’t be trusted in emergency situations. AI systems can spot complex patterns in massive data sets but still lack the common sense of a child or the innate language skills of a two-year-old. There are still very difficult technical challenges ahead. But if AI is going to achieve its full economic potential, we’ll need to pay as much attention to the social and employment challenges as we do to the technical ones.
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Full automation commonly defined as requiring no control or very limited control by the driver; such automation would be accomplished through a combination of sensor, computer, and communications systems in vehicles and along the roadway. Fully automated driving would, in theory, allow closer vehicle spacing and higher speeds, which could enhance traffic capacity in places where additional road building is physically impossible, politically unacceptable, or prohibitively expensive. Automated controls also might enhance road safety by reducing the opportunity for driver error, which causes a large share of motor vehicle crashes. Other potential benefits include improved air quality (as a result of more-efficient traffic flows), increased fuel economy, and spin-off technologies generated during research and development related to automated highway systems.[71]
Considering all of its shortcomings, we are lucky that testing existing functionality isn’t really testing. As we said before, real testing is questioning each and every aspect and underlying assumption of the product. Existing functionality has already endured that sort of testing. Although it might be necessary to re-evaluate assumptions that were considered valid at the time of testing, this is typically not necessary before every release and certainly not continuously. Testing existing functionality is not really testing. It is called regression testing, and although it sounds the same, regression testing is to testing like pet is to carpet—not at all related. The goal of regression testing is merely to recheck that existing functionality still works as it did at the time of the actual testing. So regression testing is about controlling the changes of the behaviour of the software. In that regard it has more to do with version control than with testing. In fact, one could say that regression testing is the missing link between controlling changes of the static properties of the software (configuration and code) and controlling changes of the dynamic properties of the software (the look and behaviour). Automated tests simply pin those dynamic properties down and transform them to a static artefact (e.g. a test script), which again can be governed by current version control systems.

“There are millions of regression tests for a Windows 10 release. For example, if you plan 10 new features, five [of those 10] are critical and a priority. These test cases will be the criteria used to release the software. You build from that progress. So on the next release, you have new features, 10 are determined critical for testing. So it keeps adding, now you have 15 regression tests being automated to keep up with the release schedules.”
Many test automation tools provide record and playback features that allow users to interactively record user actions and replay them back any number of times, comparing actual results to those expected. The advantage of this approach is that it requires little or no software development. This approach can be applied to any application that has a graphical user interface. However, reliance on these features poses major reliability and maintainability problems. Relabelling a button or moving it to another part of the window may require the test to be re-recorded. Record and playback also often adds irrelevant activities or incorrectly records some activities.[citation needed]
It is “glaringly obvious,” says Daron Acemoglu, an economist at MIT, that political leaders are “totally unprepared” to deal with how automation is changing employment. Automation has been displacing workers from a variety of occupations, including ones in manufacturing. And now, he says, AI and the quickening deployment of robots in various industries, including auto manufacturing, metal products, pharmaceuticals, food service, and warehouses, could exacerbate the effects. “We haven’t even begun the debate,” he warns. “We’ve just been papering over the issues.”
BPA is sometimes referred to as information technology process automation (ITPA). Implementing BPA can be a major event; because many business IT environments are virtual or cloud-based, their complexity can be challenging. Furthermore, in business process management (BPM), the automation element can take a backseat to defining the processes themselves. BPA concentrates on first automating the processes, then analyzing and optimizing them. BPA practitioners know that business needs change rapidly and there’s often no time for substantial business process modeling and mapping projects prior to software selection.