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Four-Step Approach Model of Inspection (FAMI) for Effective Defect Management in Software Development  [PDF]
Suma V.,T. R. Gopalakrishnan Nair
Computer Science , 2012,
Abstract: IT industry should inculcate effective defect management on a continual basis to deploy nearly a zerodefect product to their customers. Inspection is one of the most imperative and effective strategies of defect management. Nevertheless, existing defect management strategies in leading software industries are successful to deliver a maximum of 96% defect-free product. An empirical study of various projects across several service-based and product-based industries proves the above affirmations. This paper provides an enhanced approach of inspection through a Four-Step Approach Model of Inspection (FAMI). FAMI consists of i) integration of Inspection Life Cycle in V-model of software development, ii) implementation of process metric Depth of Inspection (DI), iii) implementation of people metric Inspection Performance Metric (IPM), iv) application of Bayesian probability approach for selection of appropriate values of inspection affecting parameters to achieve the desirable DI. The managers of software houses can make use of P2 metric as a benchmarking tool for the projects in order to improve the in-house defect management process. Implementation of FAMI in software industries reflects a continual process improvement and leads to the development of nearly a zero-defect product through effective defect management.
Estimation of Characteristics of a Software Team for Implementing Effective Inspection Process through Inspection Performance Metric  [PDF]
T. R. Gopalakrishnan Nair,Suma. V
Computer Science , 2011,
Abstract: The continued existence of any software industry depends on its capability to develop nearly zero-defect product, which is achievable through effective defect management. Inspection has proven to be one of the promising techniques of defect management. Introductions of metrics like, Depth of Inspection (DI, a process metric) and Inspection Performance Metric (IPM, a people metric) enable one to have an appropriate measurement of inspection technique. This article elucidates a mathematical approach to estimate the IPM value without depending on shop floor defect count at every time. By applying multiple linear regression models, a set of characteristic coefficients of the team is evaluated. These coefficients are calculated from the empirical projects that are sampled from the teams of product-based and service-based IT industries. A sample of three verification projects indicates a close match between the IPM values obtained from the defect count (IPMdc) and IPM values obtained using the team coefficients using the mathematical model (IPMtc). The IPM values observed onsite and IPM values produced by our model which are strongly matching, support the predictive capability of IPM through team coefficients. Having finalized the value of IPM that a company should achieve for a project, it can tune the inspection influencing parameters to realize the desired quality level of IPM. Evaluation of team coefficients resolves several defect-associated issues, which are related to the management, stakeholders, outsourcing agents and customers. In addition, the coefficient vector will further aid the strategy of PSP and TSP
Software Defect Management Using a Comprehensive Software Inspection Model
Software Engineering , 2012, DOI: 10.5923/j.se.20120204.09
Abstract: Traditional inspection approaches that are used for more than three decades are not effective for current software and development processes. The studies and experiments by testing and inspection professionals showed that customizing inspections can increase their effectiveness as well as efficiency. The comprehensive software inspection model in this article performs defect removal actions as an important duty of inspection, as well as, using the capabilities of collaborative and knowledge base systems. The process improvement is continuously in progress by creating swap iteration in inspection model kernel. In order to validate the model, it is implemented in a real software inspection project. The varieties of detected and removed defects show the potential performance of the model.
Automated Fabric Defect Inspection: A Survey of Classifiers  [PDF]
Md. Tarek Habib,Rahat Hossain Faisal,M. Rokonuzzaman,Farruk Ahmed
Computer Science , 2014, DOI: 10.5121/ijfcst.2014.4102
Abstract: Quality control at each stage of production in textile industry has become a key factor to retaining the existence in the highly competitive global market. Problems of manual fabric defect inspection are lack of accuracy and high time consumption, where early and accurate fabric defect detection is a significant phase of quality control. Computer vision based, i.e. automated fabric defect inspection systems are thought by many researchers of different countries to be very useful to resolve these problems. There are two major challenges to be resolved to attain a successful automated fabric defect inspection system. They are defect detection and defect classification. In this work, we discuss different techniques used for automated fabric defect classification, then show a survey of classifiers used in automated fabric defect inspection systems, and finally, compare these classifiers by using performance metrics. This work is expected to be very useful for the researchers in the area of automated fabric defect inspection to understand and evaluate the many potential options in this field.
Automated Defect Inspection Systems by Pattern Recognition  [PDF]
Mira Park,Jesse S. Jin,Sherlock L. Au,Suhuai Luo
International Journal of Signal Processing, Image Processing and Pattern Recognition , 2009,
Abstract: Visual inspection and classification of cigarettes packaged in a tin container is very important in manufacturing cigarette products that require high quality package presentation. For accurate automated inspection and classification, computer vision has been deployed widely in manufacturing. We present the detection of the defective packaging of tins of cigarettes by identifying individual objects in the cigarette tins. Object identification information is used for the classification of the acceptable cases (correctly packaged tins) or defective cases (incorrectly packaged tins). This paper investigates the problem of identifying the individual cigarettes and a paper spoon in the packaged tin using image processing andmorphology operations. The segmentation performance was evaluated on 500 images including examples of both good cases and defective cases.
Online Fabric Defect Inspection Using Smart Visual Sensors  [PDF]
Yundong Li,Jingxuan Ai,Changqing Sun
Sensors , 2013, DOI: 10.3390/s130404659
Abstract: Fabric defect inspection is necessary and essential for quality control in the textile industry. Traditionally, fabric inspection to assure textile quality is done by humans, however, in the past years, researchers have paid attention to PC-based automatic inspection systems to improve the detection efficiency. This paper proposes a novel automatic inspection scheme for the warp knitting machine using smart visual sensors. The proposed system consists of multiple smart visual sensors and a controller. Each sensor can scan 800 mm width of web, and can work independently. The following are considered in dealing with broken-end defects caused by a single yarn: first, a smart visual sensor is composed of a powerful DSP processor and a 2-megapixel high definition image sensor. Second, a wavelet transform is used to decompose fabric images, and an improved direct thresholding method based on high frequency coefficients is proposed. Third, a proper template is chosen in a mathematical morphology filter to remove noise. Fourth, a defect detection algorithm is optimized to meet real-time demands. The proposed scheme has been running for six months on a warp knitting machine in a textile factory. The actual operation shows that the system is effective, and its detection rate reaches 98%.
Defect Inspection of Flip Chip Solder Bumps Using an Ultrasonic Transducer  [PDF]
Lei Su,Tielin Shi,Zhensong Xu,Xiangning Lu,Guanglan Liao
Sensors , 2013, DOI: 10.3390/s131216281
Abstract: Surface mount technology has spurred a rapid decrease in the size of electronic packages, where solder bump inspection of surface mount packages is crucial in the electronics manufacturing industry. In this study we demonstrate the feasibility of using a 230 MHz ultrasonic transducer for nondestructive flip chip testing. The reflected time domain signal was captured when the transducer scanning the flip chip, and the image of the flip chip was generated by scanning acoustic microscopy. Normalized cross-correlation was used to locate the center of solder bumps for segmenting the flip chip image. Then five features were extracted from the signals and images. The support vector machine was adopted to process the five features for classification and recognition. The results show the feasibility of this approach with high recognition rate, proving that defect inspection of flip chip solder bumps using the ultrasonic transducer has high potential in microelectronics packaging.
Quality management system modelling of vehicle inspection stations
Transport Problems : an International Scientific Journal , 2009,
Abstract: The subject of the article is problems of quality assurance modelling at the Vehicle Inspection Stations (SKP). The essence of the quality management and assurance at the SKP has been presented as well as their influence on efficiency and effectiveness of SKP activities, to meet the clients’ quality requirements. Two models were described: process and neural quality assurance at the SKP.
Guiding Testing Activities by Predicting Defect-prone Parts Using Product and Inspection Metrics  [PDF]
Frank Elberzhager,Stephan Kremer,Jürgen Münch,Danilo Assmann
Computer Science , 2013, DOI: 10.1109/SEAA.2012.30
Abstract: Product metrics, such as size or complexity, are often used to identify defect-prone parts or to focus quality assurance activities. In contrast, quality information that is available early, such as information provided by inspections, is usually not used. Currently, only little experience is documented in the literature on whether data from early defect detection activities can support the identification of defect-prone parts later in the development process. This article compares selected product and inspection metrics commonly used to predict defect-prone parts. Based on initial experience from two case studies performed in different environments, the suitability of different metrics for predicting defect-prone parts is illustrated. These studies revealed that inspection defect data seems to be a suitable predictor, and a combination of certain inspection and product metrics led to the best prioritizations in our contexts.
In-TFT-Array-Process Micro Defect Inspection Using Nonlinear Principal Component Analysis  [PDF]
Yi-Hung Liu,Chi-Kai Wang,Yung Ting,Wei-Zhi Lin,Zhi-Hao Kang,Ching-Shun Chen,Jih-Shang Hwang
International Journal of Molecular Sciences , 2009, DOI: 10.3390/ijms10104498
Abstract: Defect inspection plays a critical role in thin film transistor liquid crystal display (TFT-LCD) manufacture, and has received much attention in the field of automatic optical inspection (AOI). Previously, most focus was put on the problems of macro-scale Mura-defect detection in cell process, but it has recently been found that the defects which substantially influence the yield rate of LCD panels are actually those in the TFT array process, which is the first process in TFT-LCD manufacturing. Defect inspection in TFT array process is therefore considered a difficult task. This paper presents a novel inspection scheme based on kernel principal component analysis (KPCA) algorithm, which is a nonlinear version of the well-known PCA algorithm. The inspection scheme can not only detect the defects from the images captured from the surface of LCD panels, but also recognize the types of the detected defects automatically. Results, based on real images provided by a LCD manufacturer in Taiwan, indicate that the KPCA-based defect inspection scheme is able to achieve a defect detection rate of over 99% and a high defect classification rate of over 96% when the imbalanced support vector machine (ISVM) with 2-norm soft margin is employed as the classifier. More importantly, the inspection time is less than 1 s per input image.
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