State-of-the-Art Object-Oriented Metrics and Its Reusability: A Decade Review

Author(s):  
Neelamadhab Padhy ◽  
Suresh Satapathy ◽  
R. P. Singh
2022 ◽  
Vol 6 (POPL) ◽  
pp. 1-29
Author(s):  
Minseok Jeon ◽  
Hakjoo Oh

In this paper, we challenge the commonly-accepted wisdom in static analysis that object sensitivity is superior to call-site sensitivity for object-oriented programs. In static analysis of object-oriented programs, object sensitivity has been established as the dominant flavor of context sensitivity thanks to its outstanding precision. On the other hand, call-site sensitivity has been regarded as unsuitable and its use in practice has been constantly discouraged for object-oriented programs. In this paper, however, we claim that call-site sensitivity is generally a superior context abstraction because it is practically possible to transform object sensitivity into more precise call-site sensitivity. Our key insight is that the previously known superiority of object sensitivity holds only in the traditional k -limited setting, where the analysis is enforced to keep the most recent k context elements. However, it no longer holds in a recently-proposed, more general setting with context tunneling. With context tunneling, where the analysis is free to choose an arbitrary k -length subsequence of context strings, we show that call-site sensitivity can simulate object sensitivity almost completely, but not vice versa. To support the claim, we present a technique, called Obj2CFA, for transforming arbitrary context-tunneled object sensitivity into more precise, context-tunneled call-site-sensitivity. We implemented Obj2CFA in Doop and used it to derive a new call-site-sensitive analysis from a state-of-the-art object-sensitive pointer analysis. Experimental results confirm that the resulting call-site sensitivity outperforms object sensitivity in precision and scalability for real-world Java programs. Remarkably, our results show that even 1-call-site sensitivity can be more precise than the conventional 3-object-sensitive analysis.


2020 ◽  
Vol 9 (6) ◽  
pp. 3925-3931
Author(s):  
S. Sharma ◽  
D. Rattan ◽  
K. Singh

1996 ◽  
Vol XVI (5) ◽  
pp. 48-58 ◽  
Author(s):  
William W. Pritchett

2006 ◽  
Vol 32 (3) ◽  
pp. 209-211 ◽  
Author(s):  
Naveen Sharma ◽  
Padmaja Joshi ◽  
R.K. Joshi

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.


Author(s):  
Hongyuan Zhu ◽  
Shijian Lu ◽  
Jianfei Cai ◽  
Guangqing Lee

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