The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagati...The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.展开更多
String validation routines have been widely used in many real-world applications,such as email validation and postcode validation.String test cases are adopted to test these validation routines,to identify potential d...String validation routines have been widely used in many real-world applications,such as email validation and postcode validation.String test cases are adopted to test these validation routines,to identify potential defects and security risks.Random Testing(RT)is a well-known testing approach to randomly generate string test cases from the input domain(i.e.,the set of all possible test inputs),which is simple to implement at a low cost.However,its testing effectiveness may be unsatisfactory for string validation routines.The main reason for this is that RT may have a high probability to generate invalid rather than valid string test cases,due to its randomness property.This research proposes a new RT approach based on the output types(i.e.,valid and invalid strings)for string validation routines,namely Output-type-guided Random Testing(RTO),which attempts to randomly generate both valid and invalid string test cases with a certain probability.This research performed an empirical study involving several real-world string validation routines collected from ten Java open-source projects,to investigate and compare testing performances of RT-O against the previous two widely-used RT methods.The results show that the generated string test cases by RT-O outperform test cases generated by other RT methods.展开更多
基金supported by the State Key Program of National Natural Science of China (Grant No.60532030)the New Century Excellent Talents in University (Grant No.NCET-08-0333)the Natural Science Foundation of Shandong Province (Grant No.Y2007G10)
文摘The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.
基金supported by the Science and Technology Development Fund of Macao,Macao SAR(Nos.0021/2023/RIA1 and 0046/2021/A)a Faculty Research Grant of Macao University of Science and Technology(No.FRG-22-103-FIE)supported by the National Natural Science Foundation of China(Nos.61872167 and 61502205).
文摘String validation routines have been widely used in many real-world applications,such as email validation and postcode validation.String test cases are adopted to test these validation routines,to identify potential defects and security risks.Random Testing(RT)is a well-known testing approach to randomly generate string test cases from the input domain(i.e.,the set of all possible test inputs),which is simple to implement at a low cost.However,its testing effectiveness may be unsatisfactory for string validation routines.The main reason for this is that RT may have a high probability to generate invalid rather than valid string test cases,due to its randomness property.This research proposes a new RT approach based on the output types(i.e.,valid and invalid strings)for string validation routines,namely Output-type-guided Random Testing(RTO),which attempts to randomly generate both valid and invalid string test cases with a certain probability.This research performed an empirical study involving several real-world string validation routines collected from ten Java open-source projects,to investigate and compare testing performances of RT-O against the previous two widely-used RT methods.The results show that the generated string test cases by RT-O outperform test cases generated by other RT methods.
