1. Add a testIn test-driven development, each new feature begins with writing a test. Write a test that defines a function or improvements of a function, which should be very succinct. To write a test, the developer must clearly understand the feature’s specification and requirements. The developer can accomplish this through use cases and user stories to cover the requirements and exception conditions, and can write the test in whatever testing framework is appropriate to the software environment. It could be a modified version of an existing test. This is a differentiating feature of test-driven development versus writing unit tests after the code is written: it makes the developer focus on the requirements before writing the code, a subtle but important difference.2. Run all tests and see if the new test failsThis validates that the test harness is working correctly, shows that the new test does not pass without requiring new code because the required behavior already exists, and it rules out the possibility that the new test is flawed and will always pass. The new test should fail for the expected reason. This step increases the developer’s confidence in the new test.3. Write the codeThe next step is to write some code that causes the test to pass. The new code written at this stage is not perfect and may, for example, pass the test in an inelegant way. That is acceptable because it will be improved and honed in Step 5.At this point, the only purpose of the written code is to pass the test. The programmer must not write code that is beyond the functionality that the test checks.4. Run testsIf all test cases now pass, the programmer can be confident that the new code meets the test requirements, and does not break or degrade any existing features. If they do not, the new code must be adjusted until they do.5. Refactor codeThe growing code base must be cleaned up regularly during test-driven development. New code can be moved from where it was convenient for passing a test to where it more logically belongs. Duplication must be removed. Object, class, module, variable and method names should clearly represent their current purpose and use, as extra functionality is added. As features are added, method bodies can get longer and other objects larger. They benefit from being split and their parts carefully named to improve readability and maintainability, which will be increasingly valuable later in the software lifecycle. Inheritance hierarchies may be rearranged to be more logical and helpful, and perhaps to benefit from recognized design patterns. There are specific and general guidelines for refactoring and for creating clean code. By continually re-running the test cases throughout each refactoring phase, the developer can be confident that process is not altering any existing functionality.The concept of removing duplication is an important aspect of any software design. In this case, however, it also applies to the removal of any duplication between the test code and the production code—for example magic numbers or strings repeated in both to make the test pass in Step 3.RepeatStarting with another new test, the cycle is then repeated to push forward the functionality. The size of the steps should always be small, with as few as 1 to 10 edits between each test run. If new code does not rapidly satisfy a new test, or other tests fail unexpectedly, the programmer should undo or revert in preference to excessive debugging. Continuous integration helps by providing revertible checkpoints. When using external libraries it is important not to make increments that are so small as to be effectively merely testing the library itself, unless there is some reason to believe that the library is buggy or is not sufficiently feature-complete to serve all the needs of the software under development.
What It Is?
DevOps is a set of practices that combines software development (Dev) and information-technology operations (Ops) which aims to shorten the systems development life cycle and provide continuous delivery with high software quality.
As DevOps is intended to be a cross-functional mode of working, those that practice the methodology use different sets of tools—referred to as “toolchains“—rather than a single one.These toolchains are expected to fit into one or more of the following categories, reflective of key aspects of the development and delivery process:
- Coding – code development and review, source code management tools, code merging
- Building – continuous integration tools, build status
- Testing – continuous testing tools that provide quick and timely feedback on business risks
- Packaging – artifact repository, application pre-deployment staging
- Releasing – change management, release approvals, release automation
- Configuring – infrastructure configuration and management, infrastructure as code tools
- Monitoring – applications performance monitoring, end-user experience
Some categories are more essential in a DevOps toolchain than others; especially continuous integration (e.g. Jenkins, Gitlab, Bitbucket pipelines) and infrastructure as code (e.g., Terraform, Ansible, Puppet).
Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusion and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
James Gosling, Mike Sheridan, and Patrick Naughton initiated the Java language project in June 1991. Java was originally designed for interactive television, but it was too advanced for the digital cable television industry at the time. The language was initially called Oak after an oak tree that stood outside Gosling’s office. Later the project went by the name Green and was finally renamed Java, from Java coffee. Gosling designed Java with a C/C++-style syntax that system and application programmers would find familiar.
Sun Microsystems released the first public implementation as Java 1.0 in 1996. It promised Write Once, Run Anywhere (WORA), providing no-cost run-times on popular platforms. Fairly secure and featuring configurable security, it allowed network- and file-access restrictions. Major web browsers soon incorporated the ability to run Java applets within web pages, and Java quickly became popular. The Java 1.0 compiler was re-written in Java by Arthur van Hoff to comply strictly with the Java 1.0 language specification. With the advent of Java 2 (released initially as J2SE 1.2 in December 1998 – 1999), new versions had multiple configurations built for different types of platforms. J2EE included technologies and APIs for enterprise applications typically run in server environments, while J2ME featured APIs optimized for mobile applications. The desktop version was renamed J2SE. In 2006, for marketing purposes, Sun renamed new J2 versions as Java EE, Java ME, and Java SE, respectively.
In 1997, Sun Microsystems approached the ISO/IEC JTC 1 standards body and later the Ecma International to formalize Java, but it soon withdrew from the process. Java remains a de facto standard, controlled through the Java Community Process. At one time, Sun made most of its Java implementations available without charge, despite their proprietary software status. Sun generated revenue from Java through the selling of licenses for specialized products such as the Java Enterprise System.
On November 13, 2006, Sun released much of its Java virtual machine (JVM) as free and open-source software (FOSS), under the terms of the GNU General Public License (GPL). On May 8, 2007, Sun finished the process, making all of its JVM’s core code available under free software/open-source distribution terms, aside from a small portion of code to which Sun did not hold the copyright.
Sun’s vice-president Rich Green said that Sun’s ideal role with regard to Java was as an evangelist. Following Oracle Corporation‘s acquisition of Sun Microsystems in 2009–10, Oracle has described itself as the steward of Java technology with a relentless commitment to fostering a community of participation and transparency. This did not prevent Oracle from filing a lawsuit against Google shortly after that for using Java inside the Android SDK (see the Android section). Java software runs on everything from laptops to data centers, game consoles to scientific supercomputers. On April 2, 2010, James Gosling resigned from Oracle.
In January 2016, Oracle announced that Java run-time environments based on JDK 9 will discontinue the browser plugin.
COBOL (“common business-oriented language”) is a compiled English-like computer programming language designed for business use. It is imperative, procedural and, since 2002, object-oriented. COBOL is primarily used in business, finance, and administrative systems for companies and governments. COBOL is still widely used in legacy applications deployed on mainframe computers, such as large-scale batch and transaction processing jobs. But due to its declining popularity and the retirement of experienced COBOL programmers, programs are being migrated to new platforms, rewritten in modern languages or replaced with software packages. Most programming in COBOL is now purely to maintain existing applications.
COBOL was designed in 1959 by CODASYL and was partly based on previous programming language design work by Grace Hopper, commonly referred to as “the (grand)mother of COBOL”. It was created as part of a US Department of Defense effort to create a portable programming language for data processing. It was originally seen as a stopgap, but the Department of Defense promptly forced computer manufacturers to provide it, resulting in its widespread adoption. It was standardized in 1968 and has since been revised four times. Expansions include support for structured and object-oriented programming. The current standard is ISO/IEC 1989:2014.
COBOL statements have an English-like syntax, which was designed to be self-documenting and highly readable. However, it is verbose and uses over 300 reserved words. In contrast with modern, succinct syntax like
y = x;, COBOL has a more English-like syntax (in this case,
MOVE x TO y). COBOL code is split into four divisions (identification, environment, data and procedure) containing a rigid hierarchy of sections, paragraphs and sentences. Lacking a large standard library, the standard specifies 43 statements, 87 functions and just one class.
Academic computer scientists were generally uninterested in business applications when COBOL was created and were not involved in its design; it was (effectively) designed from the ground up as a computer language for business, with an emphasis on inputs and outputs, whose only data types were numbers and strings of text. COBOL has been criticized throughout its life, for its verbosity, design process, and poor support for structured programming. These weaknesses result in monolithic and, though intended to be English-like, not easily comprehensible and verbose programs.
In computing, a stack trace (also called stack backtrace or stack traceback) is a report of the active stack frames at a certain point in time during the execution of a program. When a program is run, memory is often dynamically allocated in two places; the stack and the heap. Memory is continuously allocated on a stack but not on a heap, thus reflective of their names. Stack also refers to a programming construct, thus to differentiate it, this stack is referred to as the program’s runtime stack. Technically, once a block of memory has been allocated on the stack, it cannot be easily removed as there can be other blocks of memory that were allocated before it. Each time a function is called in a program, a block of memory is allocated on top of the runtime stack called the activation record (or stack pointer.) At a high level, an activation record allocates memory for the function’s parameters and local variables declared in the function.
Programmers commonly use stack tracing during interactive and post-mortem debugging. End-users may see a stack trace displayed as part of an error message, which the user can then report to a programmer.
A stack trace allows tracking the sequence of nested functions called – up to the point where the stack trace is generated. In a post-mortem scenario this extends up to the function where the failure occurred (but was not necessarily caused). Sibling calls do not appear in a stack trace.