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An Empirical Study on Defects in Open Source Artificial Intelligence Applications
Yoon Ho Choi, Changgong Lee, Jaechang Nam
http://doi.org/10.5626/JOK.2022.49.8.633
The differences between the programming paradigm of applications using artificial intelligence (AI) and traditional applications may show different results in detecting, understanding, analyzing, and fixing defects. In this study, we collect defects that have been reported in open source AI applications and identify common causes of the defects to understand and analyze them in AI-based systems. To this end, we analyze the defects of ten open-source AI applications archived on GitHub by inspecting 1,205 issues and defect-fixing code changes that had been reported, found, and fixed. We classified the defects into 20 categories based on their causes, which are found in at least five out of ten projects. We expect that the result of this study will provide useful information in software quality assurance approaches such as fault localization and patch suggestion.
Fast Non-Adjacent Form (NAF) Conversion through a Bit-Stream Scan
Doo-Hee Hwang, Jin-Myeong Shin, Yoon-Ho Choi
As a special form of the signed-digit representation, the NAF(non-adjacent form) minimizes the hamming weight by reducing the average density of the non-zero bits from the binary representation of the positive integer k. Due to this advantage, the NAF is used in various fields; in particular, it is actively used in cryptology. The existing NAF-conversion algorithm, however, is problematic because the conversion speed decreases when the LSB(least significant bit) frequently becomes "1" during the binary positive integer conversion process. This paper suggests a method for the improvement of the NAF-conversion speed for which the problems that occur in the existing NAF-conversion process are solved. To verify the performance improvement of the algorithm, the CPU cycle for the various inputs were measured on the ATmega128, a low-performance 8-bit microprocessor. The results of this study show that, compared with the existing algorithm, the suggested algorithm not only improved the processing speed of the major patterns by 20% or more on average, but it also reduced the NAF-conversion time by 13% or more.
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