Abstract— Software plays a significant role in various fields such as business, medicine, nuclear test etc. There is utmost need for improvement of software to solve many real time world problems. Software Engineering is a discipline aimed at creation of fault-free software that is delivered on time, within budget, and satisfying the user’s needs. Data Mining is a process which analyses data from a 360 degree perspective and then summarize it into most useful information. Data Mining Techniques can be applied to solve software engineering defects and bugs. Software development team tries to enhance software quality and efficiency by minimizing the defect ratio to the least possible extent. Software life cycle is a human activity dependent …show more content…
The more the complexity of the software the more is the chance for faults and defects occurrence which increases the cost. The magnitude of the data is very large and its complexity is very high. Hence data mining becomes an undisputed necessity in software engineering [1]. Challenges in Software Engineering include consolidation of requirements, systems integration & evolution, maintainability, discovering the patterns, flaw detection, reliability and complexity of software development [1][ 2]. A software defect is an error, flaw, mistake, failure, or fault in a computer program / system. This results in producing an incorrect result. It can lead the program to behave in undesired ways [3].Software engineers widely apply the data mining techniques, methods and algorithms to solve various software engineering problems. This is done to improve productivity and quality of …show more content…
Classification: Classification in data mining is an important task used for assigning a data item to a predefined set of classes. It is described as a function that maps a data item into one of the several predefined classes [6]. There is multitude of forms in which data exists in software engineering. By Classification is used to build defect prediction model by assimilating the already processed defect data and the use it to predict defects in future version of software. It is aimed at determining if a software module has a higher risk of defect. Classification usually assesses the data from earlier project versions as well as from similar data of other projects to establish a classification model. This model will be used to forecast the software defects. Many classification algorithms are used in software engineering to solve variety of problems under different phases. Classifications are used to identify bug types and thus help to build bug detector. A decision tree is a critical tool in classification technique that helps to identify the risky modules in software based on attributes of system and modules. Even though classification and assignment can be automated, it is often done by humans, especially when a bug is wrongly registered by the reporter in the bug
The Software Development Life Cycle is seldom used at my place of work. Unfortunately, recent developments in its use are deemed confidential. Due to this fact, this paper will examine in general terms one of the projects we are undertaking right now while at the same time attempting to maintain our confidentiality.
Classification Text documents are arranged into groups of pre-labeled class. Learning schemes learn through training text documents and efficiency of these system is tested by using test text documents. Common algorithms include decision tree learning, naive Bayesian classification, nearest neighbor and neural network. This is called supervised learning.
CMMI was developed by a group of experts from industry, government, and the Software Engineering Institute (SEI) at Carnegie Mellon University. The main sponsors ...
1.0 IntroductionIn this report I will be concentrating on the failure of software systems. To understand why software systems fail we need to understand what are software systems. Software systems are a type of information system. This is because a software system is basically a means for hardware to process information. Flynn’s definition of an information system is:"An information system provides procedures to record and make available information, concerning part of an organization, to assist organization-related activities."Humans have been processing information manually for thousands of years, but with the vast increase of demand for knowledge this century has meant that a new method of information processing has been needed. Software systems have provided a new means that is much faster and efficient.
While developing a software many complex activities are required which in turn have dependencies along them. Large software projects require the involvement of programmers, documentation specialists, program managers, architects, database analysts, system analysts, , and trainers and last but not the least testers. Along with their independent responsibilities these persons are also responsible to maintain and keep a check on the software system quality as well.
Software engineering is the application of engineering to the design, development, implementation, testing and maintenance of software in a systematic method. Knowledge of computer programming is a prerequisite for becoming a software engineer many software engineers enter the
Data is defined as facts, concepts, information, or instructions in a formalized manner suitable for communication, interpretation, or processing by humans or by automatic means. Like mentioned before this processing is usually assumed to be automated and running on a computer. Data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems. Now that we know the purpose and meaning of data, we proceed to explain what data mining consist of.
To increase software quality, developers must thoroughly test their code early in the development process. Bugs must be caught and r...
Data mining is a field that is a combination of numerous other fields such as the database research, artificial intelligence and statistics. Data mining involves looking for patterns in vast amounts of data as a part of knowledge discovery process. (Huang, Joshua Zhexue, Cao, Longbing, Srivastava, Jaideep, 2011) contains numerous papers that are solely dedicated to discussing the advancements that have been made in the field of data mining and knowledge discovery. A lot of people have performed a thorough research on all that has been done in data mining and the future possibilities that are soon to be implemented practically. The research not only covers the history and the reasons that led to various advancements being made but they also cover the detail models of the proposed solutions to deficiencies in existing systems.
Customer data mining has a vast potential, but the inner workings of this business practice are quite complex. According to Jason Frand, a Managerial Computing and Information Systems Professor at UCLA, customer data mining is a very complicated process that requires experts with a high level of understanding. Several types of analytical softwares are available for customer data mining, with the two main softwares being statistical software and machine learning software, which enables the computer to ‘learn’ from data. These softwares seek four main relationships.The first relationship sought are classes which are information stored in predeter...
Agile Methodology is a term I have come across more and more throughout my exposure to professionals in the IS field. Wither it be networking activities, interviews, or general conversation I repeatedly hear “Agile Methodology” in reference to system development and project management, where it is referenced as the “newest and greatest” method of development. This has made me wonder what exactly is Agile Methodology, when is it best for a development team to use Agile, why is Agile the “in” methodology, how is it implemented, and what are the results of “going agile”. In this series of summaries, I will peruse blogs and networking sites on the internet, utilized various journals, and interview fellow students and professionals in the IS field to discover the what, when, why, and how of Agile Methodology. In preparation of this summary I found that Agile is an umbrella term for at least ten different methodologies with three common key principles based on the Agile Manifesto.
This paper details the ID3 classification algorithm. Very simply, ID3 builds a decision tree from a fixed set of examples. The resulting tree is used to classify future samples. The example has several attributes and belongs to a class (like yes or no). The leaf nodes of the decision tree contain the class name whereas a non-leaf node is a decision node. The decision node is an attribute test with each branch (to another decision tree) being a possible value of the attribute. ID3 uses information gain to help it decide which attribute goes into a decision node. The advantage of learning a decision tree is that a program, rather than a knowledge engineer, elicits knowledge from an expert.
Software reverse engineering originated in software maintenance, it has been successfully solving numerous software engineering problems, such as recovering architectures and design patterns, documenting programs and database, computing change impacts, re-modularizing existing systems, etc.
The key objective in any data mining activity is to find as many unsuspected relationships between obtained data sets as possible to be able to achieve a better understanding on how the data and its relationships are useful to the data owner. The potential of knowledge discovery using data mining is huge and data mining has been applied in many different knowledge areas such as in large corporations to optimize their marketing strategies or even to smaller scale in medicinal research where data mining is used to find the relationship patient’s data with the corresponding medicinal prescription and symptoms.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.