Orthogonal Defect Classification
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An approach involving automatic and human interpretation of defect data to correct software production processes during development is becoming important at IBM to improve quality and productivity. The paper uses examples from interpreting defect data to evaluate and evolve this approach, informing and teaching those who will use it in software development. The methodology is shown to benefit different product types beyond current practices, representing experiences from using a model of process correction.
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International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity
Manufacturing through system dynamic
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Defect analysis and prevention methods
The document discusses defect analysis and prevention. It defines key terms like errors, defects, and failures. It describes the defect analysis procedure which includes forming a causal analysis team to identify root causes of defects so they can be prevented. The team proposes actions, while an action team implements solutions. Data on defect types and trends is analyzed to prioritize issues. Tools like fishbone diagrams may be used to sort contributing factors. The goal is to systematically eliminate common causes of defects.
AUTOMATED BUG TRIAGE USING ADVANCED DATA REDUCTION TECHNIQUES
Bug triage is an important step in the process of bug fixing. The goal of bug triage is to correctly assign a developer to a newly reported bug in the system. To perform the automated bug triage, text classification techniques are applied. This will helps to reduce the time cost in manual work. To reduce the scale and improve the quality of bug data, the proposed system addresses the data reduction techniques, instance selection and feature selection for bug triage. The instance selection technique used here is to identify the relevant bugs that can match the newly reported bug. The feature selection technique is used to select the relevant data from each bug in the training set. A predictive model is proposed to identify the order in which the data reduction techniques are applied for each newly reported bug. This step will improve the performance of the classification process. An experimental study using Eclipse and Firefox bug data is undergone in which the proposed system shows an accuracy of 73%.
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The document discusses defect analysis and prevention. It defines key terms like errors, defects, and failures. It describes the defect analysis procedure which includes forming a causal analysis team to identify root causes of defects so they can be prevented. The team proposes actions, while an action team implements solutions. Data on defect types and trends is analyzed to prioritize issues. Tools like fishbone diagrams may be used to sort contributing factors. The goal is to systematically eliminate common causes of defects.
AUTOMATED BUG TRIAGE USING ADVANCED DATA REDUCTION TECHNIQUES
Bug triage is an important step in the process of bug fixing. The goal of bug triage is to correctly assign a developer to a newly reported bug in the system. To perform the automated bug triage, text classification techniques are applied. This will helps to reduce the time cost in manual work. To reduce the scale and improve the quality of bug data, the proposed system addresses the data reduction techniques, instance selection and feature selection for bug triage. The instance selection technique used here is to identify the relevant bugs that can match the newly reported bug. The feature selection technique is used to select the relevant data from each bug in the training set. A predictive model is proposed to identify the order in which the data reduction techniques are applied for each newly reported bug. This step will improve the performance of the classification process. An experimental study using Eclipse and Firefox bug data is undergone in which the proposed system shows an accuracy of 73%.
Agile maintenance
The document discusses how agile methods can and should be applied to software maintenance, not just development. It provides several examples and studies that show how agile practices like Extreme Programming (XP) improve maintenance by reducing costs and issues while improving quality, compared to traditional waterfall methods. Open source software development is also discussed as a natural fit for agile maintenance practices.
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Bug triage means to transfer a new bug to expertise developer. The manual bug triage is opulent in time
and poor in accuracy, there is a need to automatize the bug triage process. In order to automate the bug triage
process, text classification techniques are applied using stopword removal and stemming. In our proposed work
we have used NB-Classifiers to predict the developers. The data reduction techniques like instance selection
and keyword selection are used to obtain bug report and words. This will help the system to predict only those
developers who are expertise in solving the assigned bug. We will also provide the change of status of bug
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profitability, and improving the quality besides cutting down the non value added costs greatly. This paper is
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success factors or practices that are responsible for the decisive role to overture the process are identified.
These practices are interchangeably called as sub-attributes. These practices must have evolved from different
strategies. The sub-attributes are quantified using least square multi attribute decision model (LSMADM) for
three alternatives strategies viz. corrective maintenance, reliability centered maintenance(RCM), and TPM. Any
sub-attribute irrespective of its own high or low relative score among the number of sub attributes is evaluated
over three alternative strategies. To implement any sub-attribute, an investigation of its highest relative score
for given alternatives will guide the managers to opt the best alternative. The best practices must come from
different strategies to get most optimal results. The priorities established using LSMADM will act as base line
to implement the industrial activities in a more systematic and balanced way to gain far-reaching optimized
productivity and quality standards. The higher priority task will be given higher consideration in terms of
committing the resources vis a vis less priority task. This will aid in orienting the collective efforts for optimal
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Operating system (Os) is the most essential software of the computer system,deprived ofit, the computer system is totally useless. It is the frontier for assessing relevant computer resources. It performance greatly
enhances user overall objective across the system. Related literatures have try in different methods and techniques to measure the process matric performance of the operating system but none has incorporated the use of genetic algorithm and fuzzy logic in their varied techniques which indeed is a novel approach. Extending the work of Michalis, this research focuses on measuring the process matrix performance of an
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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code, are calculated for each UML of the projects. Based on the nominal values of metrics, each relevant
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strategies. The sub-attributes are quantified using least square multi attribute decision model (LSMADM) for
three alternatives strategies viz. corrective maintenance, reliability centered maintenance(RCM), and TPM. Any
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over three alternative strategies. To implement any sub-attribute, an investigation of its highest relative score
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A NOVEL APPROACH TO ERROR DETECTION AND CORRECTION OF C PROGRAMS USING MACHIN...
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syntactical or logical error. This struggle has led to a research in identification of syntactical and logical
errors. This paper makes an attempt to survey those research works which can be used to identify errors as
well as proposes a new model based on machine learning and data mining which can detect logical and
syntactical errors by correcting them or providing suggestions. The proposed work is based on use of
hashtags to identify each correct program uniquely and this in turn can be compared with the logically
incorrect program in order to identify errors.
GENETIC-FUZZY PROCESS METRIC MEASUREMENT SYSTEM FOR AN OPERATING SYSTEM
This document presents a genetic-fuzzy system for measuring the performance of an operating system's processes. It develops a model using 7 key operating system process parameters and fuzzy logic to handle imprecision. A genetic algorithm is used to optimize the generated membership functions. Rules are created relating parameter combinations to performance classifications. The system was tested on sample data and the genetic algorithm was able to optimize the membership functions over 4 generations to best classify performance. The system brings an optimal and precise approach to measuring operating system process performance by combining genetic algorithms and fuzzy logic.
Genetic fuzzy process metric measurement system for an operating system
Operating system (Os) is the most essential software of the computer system,deprived ofit, the computer
system is totally useless. It is the frontier for assessing relevant computer resources. It performance greatly
enhances user overall objective across the system. Related literatures have try in different methods and
techniques to measure the process matric performance of the operating system but none has incorporated
the use of genetic algorithm and fuzzy logic in their varied techniques which indeed is a novel approach.
Extending the work of Michalis, this research focuses on measuring the process matrix performance of an
operating system utilizing set of operating system criteria’s while fusing fuzzy logic to handle
impreciseness and genetic for process optimization.
GENETIC-FUZZY PROCESS METRIC MEASUREMENT SYSTEM FOR AN OPERATING SYSTEM
Operating system (Os) is the most essential software of the computer system,deprived ofit, the computer system is totally useless. It is the frontier for assessing relevant computer resources. It performance greatly
enhances user overall objective across the system. Related literatures have try in different methods and techniques to measure the process matric performance of the operating system but none has incorporated the use of genetic algorithm and fuzzy logic in their varied techniques which indeed is a novel approach. Extending the work of Michalis, this research focuses on measuring the process matrix performance of an
operating system utilizing set of operating system criteria’s while fusing fuzzy logic to handle impreciseness and genetic for process optimization.
Smart Sim Selector: A Software for Simulation Software Selection
In a period of continuous change in global business environment, organizations, large and small, are finding it increasingly difficult to deal with, and adjust to the demands for such change. Simulation is a powerful tool for allowing designers imagine new systems and enabling them to both quantify and observe behavior. Currently the market offers a variety of simulation software packages. Some are less expensive than others. Some are generic and can be used in a wide variety of application areas while others are more specific. Some have powerful features for modeling while others provide only basic features. Modeling approaches and strategies are different for different packages. Companies are seeking advice about the desirable features of software for manufacturing simulation, depending on the purpose of its use. Because of this, the importance of an adequate approach to simulation software selection is apparent. Smart Sim Selector is a software developed for the purpose of providing support for users when selecting simulation software. Smart Sim Selector consists of a database which is linked to an interface developed using Visual Basic 6.0. The system queries a database and finds a simulation package suitable to the user, based on requirements which have been specified. This paper provides an insight into the development of Smart Sim Selector, in addition to the reasoning behind the system.
Similar to Orthogonal Defect Classification (20)
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A NOVEL APPROACH TO ERROR DETECTION AND CORRECTION OF C PROGRAMS USING MACHIN...
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GENETIC-FUZZY PROCESS METRIC MEASUREMENT SYSTEM FOR AN OPERATING SYSTEM
GENETIC-FUZZY PROCESS METRIC MEASUREMENT SYSTEM FOR AN OPERATING SYSTEM
Genetic fuzzy process metric measurement system for an operating system
Genetic fuzzy process metric measurement system for an operating system
GENETIC-FUZZY PROCESS METRIC MEASUREMENT SYSTEM FOR AN OPERATING SYSTEM
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Smart Sim Selector: A Software for Simulation Software Selection
Smart Sim Selector: A Software for Simulation Software Selection
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
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Orthogonal Defect Classification
- 1. IBM Systems Journal, Vol. 33, No. 1, 1994. IN-PROCESS IMPROVEMENT THROUGH DEFECT DATA INTERPRETATION Inderpal Bhandari, Michael Halliday, Jarir Chaar, Ram Chillarege, Kevin Jones, Janette Atkinson, Cleo Lepori- Costello , Pamela Jasper, Eric Tarver, Cecilia Carranza Lewis, Masato Yonezawa Abstract: An approach that involves both automatic and human interpretation to correct the software production process during development is becoming important in IBM as a means to improve quality and productivity. A key step of the approach is the interpretation of defect data by the project team. This paper uses examples of such correction to evaluate and evolve the approach, and to inform and teach those who will use the approach in software development. The methodology is shown to benefit different kinds of products beyond what can be achieved by current practices, and the collection of examples discussed represents the experiences of using a model of correction. To order full article, go to: http://domino.research.ibm.com/tchjr/journalindex.nsf/600cc5649e2871db852568150060213c/82998bce378566fc85256bfa00685cc9?OpenDocument