Empirical methods used in software engineering
Empirical software engineering is a related concept, sometimes used synonymously with experimental software engineering. Empirical software engineering is a field of research that emphasizes the use of empirical studies of all kinds to accumulate knowledge. Methods used include
These methods can be categorized into
The Scientific method is a way to make sure that experiment can give a good answer to specific question. The Scientific Method is a logical and rational order of steps by which scientists come to conclusions about the world around them. The Scientific Method helps to organize thoughts and procedures so that scientists can be confident in the answers they find. Scientists use observations, hypotheses, and deductions to make these conclusions. Various possibilities are thought through by using the Scientific Method to eventually come to an answer to the original question.
The steps of the Scientific Method are:
OBSERVATION
This step could also be called "research." It is the first stage in understanding the chosen problem. After decision of area of research and the specific question to ask, it is needed to research everything that can be found about the problem. Information on topic can be collected from experiences, books, the internet, or even smaller "unofficial" experiments. This initial research should play a big part in the idea that has been chosen.
For this stage of the Scientific Method, it''s important to use as many sources as we can find. The more information on project topic, the better the design of the experiment is going to be, and the better the project is going to be overall.
HYPOTHESIS
The next stage of the Scientific Method is known as the "hypothesis." This word basically means "a possible solution to a problem, based on knowledge and research." The hypothesis is a simple statement that defines that what the expected outcome of the experiment will be. All of the first stage of the Scientific Method -- the observation, or research stage -- is designed to express a problem in a single question ("Does the amount of sunlight in a garden affect tomato size?") and the experiment is designed to test the hypothesis.
PREDICTION
Prediction lets get specific -- how to demonstrate that the hypothesis is true? The experiment is designed to test the prediction.
An important thing to remember during this stage of the scientific method is that once a hypothesis and a prediction, is developed, it shouldn''t be changed, even if the results of experiment show that it is wrong. An incorrect prediction doesn''t mean the “failure" It just means that the experiment brought some new facts to light that maybe hadn''t thought about before.
EXPERIMENTATION
This is the part of the scientific method that tests your hypothesis. An experiment is a tool that is designed to find out if the ideas about the topic are right or wrong. It is absolutely necessary to design an experiment that will accurately test the hypothesis. The experiment is the most important part of the scientific method. It''s the logical process that lets scientists learn about the world.
CONCLUSION The final step in the scientific method is the conclusion. This is a summary of the experiment''s results, and how those results match up to the hypothesis.
Models of scientific inquiry Classical model The classical model of scientific inquiry derives from Aristotle, who distinguished the forms of approximate and exact reasoning, set out the threefold scheme of abductive, deductive, and inductive inference, and also treated the compound forms such as reasoning by analogy.
Pragmatic model Charles Peirce considered scientific inquiry to be a species inquiry, which he defined as any means of fixing belief, that is, any means of arriving at a settled opinion on a matter in question. He observed that inquiry in general begins with a state of uncertainty and moves toward a state of certainty, sufficient at least to terminate the inquiry for the time being. He graded the prevalent forms of inquiry according to their evident success in achieving their common objective, scoring scientific inquiry at the high end of this scale. At the low end he placed what he called the method of tenacity, a die-hard attempt to deny uncertainty and fixate on a favored belief. Next in line he placed the method of authority, a determined attempt to conform to a chosen source of ready-made beliefs. After that he placed what might be called the method of congruity, also called the a priori, the dilettante, or what is agreeable to reason method.
Computational approaches Many subspecialties of applied logic and computer science, to name a few, artificial intelligence, machine learning, computational learning theory, inferential statistics, and knowledge representation, are concerned with setting out computational, logical, and statistical frameworks for the various types of inference involved in scientific inquiry, in particular, hypothesis formation, logical deduction, and empirical testing. Some of these applications draw on measures of complexity from algorithmic information theory to guide the making of predictions from prior distributions of experience, for example, see the complexity measure called the speed prior from which a computable strategy for optimal inductive reasoning can be derived.
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Published: November 02, 2007
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