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Biology insists — and common sense says — that I've started to become that old fogey I used to laugh at in my younger days.
...THIRD YORKSHIREMAN:
Well, of course, we had it tough. We used to 'ave to get up out of shoebox at twelve o'clock at night and lick road clean wit' tongue. We had two bits of cold gravel, worked twenty-four hours a day at mill for sixpence every four years, and when we got home our Dad would slice us in two wit' bread knife.
Have you ever wondered how data flows within a software system? How is information processed and transformed, and how does it deliver value? Data Flow Diagrams (DFDs) are a "visual language" that may answer such questions. An important tool for understanding how data moves in a software system, DFDs provide a visual representation of the flow of data from its entry point to its final destination and highlight data transformations along the way.
Whether you're a tester, a seasoned developer, a budding programmer, or a stakeholder involved in system design and architecture, understanding DFDs unlocks a valuable skillset. This article provides fundamental knowledge about DFDs, highlighting their benefits and guiding you on how to leverage them effectively.
Fuzzing, also known as fuzz testing, is an automated software testing technique that involves providing invalid, unexpected, or random data (fuzz) as inputs to a computer program. The goal is to find coding errors, bugs, security vulnerabilities, and loopholes that can be exploited. This article starts by explaining some basic types of fuzzing. The "testing the lock" metaphor is then used to explain the nuts and bolts of this technique. A list of available tools is given and a set of best practices are explored for fuzzing to be conducted ethically, effectively, and safely.
Fuzzing, as a versatile software testing technique, can be categorized into several types based on the methodology and the level of knowledge about the software being tested. Each type of fuzzing has its unique approach and is suitable for different testing scenarios.
As the global community grapples with the urgent challenges of climate change, the role of technology and software becomes increasingly pivotal in the quest for sustainability. There exist optimization approaches at multiple levels that can help:
In this article, we will focus on code optimization via software testing. Software testing, a fundamental component of software development, can play a significant role in mitigating the environmental impact of technology. We explore the intersection of software testing and climate change, highlighting how testing can contribute to a more sustainable technological landscape. We begin by summarizing the role of software in the energy footprint of a number of industries. We then explore basic types of software testing that can be applied, giving specific examples. These types are by no means exhaustive. Other types of testing may well be used according to the energy optimization scenario.
Begin by selecting a programming language that you feel comfortable with and that you will use for your interviews. It can be any language, such as Java or Python, as long as you have a solid understanding and knowledge of it.
Acquiring a thorough understanding of popular data structures and algorithms is crucial. I recommend picking up a book like “Data Structures and Algorithms in Java” by Robert Lafore and mastering the fundamentals of data structures and algorithms. Although interviewers may not frequently ask you to implement them, practicing and comprehending their runtime and space complexities is essential.
While Artificial intelligence (AI) and Software Engineering are two major branches of computer sciences, experts and professionals have consistently acknowledged their differences and the roles they both play in the advancements of computer efficiency generally. However, while there are differences between the two fields, people have difficulty telling where they differ. Therefore, this blog will outline the differences between AI and Software Engineering to help you know the varying metrics.
The biggest difference between software engineering and Artificial intelligence is their outcomes and the tasks they set out to achieve.
It has been widely acknowledged that coding is an art, and like every artisan who crafts wonderful art and is proud of them, we as developers are also really proud of the code we write. In order to achieve the best results, artists constantly keep searching for ways and tools to improve their craft. Similarly, we as developers keep leveling up our skills and remain curious to know the answer to the single most important question — 'How to write good code.'
Frederick P. Brooks in his book 'The Mythical Man Month: Essays on Software Engineering' wrote:
The world’s greatest chefs never put anything on the plate that will never be eaten, this rule corresponds to the YAGNI principle in Software Engineering. The non-used code blocks saved for future use are sometimes forgotten and another developer could not be in want of changing the code of the legacy system. Such non-used code blocks may affect code readability in the future. We are lucky today many code review tools exist for coding mistakes etc., but using tools is not enough to help code readability.
The spiral model is a combination of waterfall and iterative development process with emphasizing on more risk analysis. Risk is essentially any adverse circumstance that might hamper the successful completion of a software project. For example, the risk involved in accessing data from a remote database can be that the data access rate might be too slow. The risk can be resolved by building a prototype of the data access subsystem. Thus, this model provides direct support for coping with the project risks.
It has planning, risk analysis, engineering and evaluation phase. Each phase in the spiral model begins with a design goal and ends with the client reviewing the progress. The development team in Spiral-SDLC model starts with a small set of requirements and goes through each development phase for those set of requirements. The development team adds functionality for the additional requirement in every-increasing spirals until the application is ready for the production phase.
According to the latest estimates, seventeen percent of organizations adopt Lean. This framework remains one of the five most widely used Agile frameworks. The application of Lean principles to software development was initially introduced by Mary and Tom Poppendieck in their book Lean Software Development: An Agile Toolkit. It includes the 7 basic principles:
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