Testing-effort（TE） and imperfect debugging（ID） in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models（SRGMs）. For describing the S-shaped...Testing-effort（TE） and imperfect debugging（ID） in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models（SRGMs）. For describing the S-shaped varying trend of TE increasing rate more accurately, first, two S-shaped testing-effort functions（TEFs）, i.e.,delayed S-shaped TEF（DS-TEF） and inflected S-shaped TEF（IS-TEF）, are proposed. Then these two TEFs are incorporated into various types（exponential-type, delayed S-shaped and inflected S-shaped） of non-homogeneous Poisson process（NHPP）SRGMs with two forms of ID respectively for obtaining a series of new NHPP SRGMs which consider S-shaped TEFs as well as ID. Finally these new SRGMs and several comparison NHPP SRGMs are applied into four real failure data-sets respectively for investigating the fitting and prediction power of these new SRGMs.The experimental results show that：（i） the proposed IS-TEF is more suitable and flexible for describing the consumption of TE than the previous TEFs;（ii） incorporating TEFs into the inflected S-shaped NHPP SRGM may be more effective and appropriate compared with the exponential-type and the delayed S-shaped NHPP SRGMs;（iii） the inflected S-shaped NHPP SRGM considering both IS-TEF and ID yields the most accurate fitting and prediction results than the other comparison NHPP SRGMs.展开更多
Because of the inevitable debugging lag,imperfect debugging process is used to replace perfect debugging process in the analysis of software reliability growth model.Considering neither testing-effort nor testing cove...Because of the inevitable debugging lag,imperfect debugging process is used to replace perfect debugging process in the analysis of software reliability growth model.Considering neither testing-effort nor testing coverage can describe software reliability for imperfect debugging completely,by hybridizing testing-effort with testing coverage under imperfect debugging,this paper proposes a new model named GMW-LO-ID.Under the assumption that the number of faults is proportional to the current number of detected faults,this model combines generalized modified Weibull(GMW)testing-effort function with logistic(LO)testing coverage function,and inherits GMW's amazing flexibility and LO's high fitting precision.Furthermore,the fitting accuracy and predictive power are verified by two series of experiments and we can draw a conclusion that our model fits the actual failure data better and predicts the software future behavior better than other ten traditional models,which only consider one or two points of testing-effort,testing coverage and imperfect debugging.展开更多
In recent years, to maximize the value of software testing and analysis, we have proposed the methodology of cooperative software testing and analysis （in short as cooperative testing and analysis） to enable testing...In recent years, to maximize the value of software testing and analysis, we have proposed the methodology of cooperative software testing and analysis （in short as cooperative testing and analysis） to enable testing and analysis tools to cooperate with their users （in the form of tool-human cooperation）, and enable one tool to cooperate with another tool （in the form of tool-tool cooperation）. Such cooperations are motivated by the observation that a tool is typically not powerful enough to address complications in testing or analysis of complex real-world software, and the tool user or another tool may be able to help out some problems faced by the tool. To enable tool-human or tool-tool cooperation, effective mechanisms need to be developed 1） for a tool to communicate problems faced by the tool to the tool user or another tool, and 2） for the tool user or another tool to assist the tool to address the problems. Such methodology of cooperative testing and analysis forms a new research frontier on synergistic cooperations between humans and tools along with cooperations between tools and tools. This article presents recent example advances and challenges on cooperative testing and analysis.展开更多
软件规模和复杂程度的不断提高,为软件质量保障带来了严峻的挑战.软件缺陷定位是一种重要的软件质量保障技术,其中基于频谱的缺陷定位(Spectrum-based Fault Localization,SFL)是应用最为广泛的软件缺陷定位技术,其通过分析语句覆盖信...软件规模和复杂程度的不断提高,为软件质量保障带来了严峻的挑战.软件缺陷定位是一种重要的软件质量保障技术,其中基于频谱的缺陷定位(Spectrum-based Fault Localization,SFL)是应用最为广泛的软件缺陷定位技术,其通过分析语句覆盖信息矩阵计算代码语句的可疑度值,并根据可疑度值定位缺陷所在语句.然而,语句覆盖信息矩阵中存在着严重的数据冗余问题,冗余的数据极大地影响了SFL的缺陷定位性能.以Defects4J数据集中395个程序的语句覆盖信息矩阵为例,在超过一半的语句覆盖信息矩阵中有90%的语句存在与其具有相同覆盖信息的语句.特征选择是常用的数据预处理技术,通过去除冗余和不相关特征来获取原始特征集中有价值的特征子集.因此,我们将语句覆盖信息矩阵作为原始特征集,将冗余覆盖信息约简建模为特征选择问题,提出了一种基于冗余覆盖信息约简的软件缺陷定位方法(Fault Localization based on Redundant coverage information Reduction,FLRR).首先,使用特征选择技术对语句覆盖信息和测试用例执行结果组成的语句覆盖信息矩阵进行约简,得到语句覆盖信息矩阵子集;然后,使用SFL计算语句覆盖信息矩阵子集中语句的可疑度值,并根据可疑度值对语句进行降序排列,以定位缺陷语句.本文使用六种常用的特征选择技术对语句覆盖信息矩阵进行特征选择和约简,以得到语句覆盖信息矩阵子集,并使用四种典型的SFL技术对语句覆盖信息矩阵子集中的语句进行缺陷定位.为评估FLRR的缺陷定位性能,本文使用E_(inspect)@n和MRR(Mean Reciprocal Rank)评价指标在基于Defects4J的数据集上与四种典型的SFL技术进行了对比实验.实验结果表明,FLRR能够有效提升SFL的缺陷定位性能.对于E_(inspect)@n指标,当n=1时,FLRR相比DStar、Ochiai、Barinel和OP2分别多定位到23条、26条、14条和13条缺陷语句,分别增加了69.70%、76.47%、45.16%和38.24%;对于MRR指标,FLRR相比DStar、Ochiai、Barinel和OP2分别提升了20.08%、24.94%、17.45%和19.15%.展开更多
基金supported by the Pre-research Foundation of CPLA General Equipment Department
文摘Testing-effort（TE） and imperfect debugging（ID） in the reliability modeling process may further improve the fitting and prediction results of software reliability growth models（SRGMs）. For describing the S-shaped varying trend of TE increasing rate more accurately, first, two S-shaped testing-effort functions（TEFs）, i.e.,delayed S-shaped TEF（DS-TEF） and inflected S-shaped TEF（IS-TEF）, are proposed. Then these two TEFs are incorporated into various types（exponential-type, delayed S-shaped and inflected S-shaped） of non-homogeneous Poisson process（NHPP）SRGMs with two forms of ID respectively for obtaining a series of new NHPP SRGMs which consider S-shaped TEFs as well as ID. Finally these new SRGMs and several comparison NHPP SRGMs are applied into four real failure data-sets respectively for investigating the fitting and prediction power of these new SRGMs.The experimental results show that：（i） the proposed IS-TEF is more suitable and flexible for describing the consumption of TE than the previous TEFs;（ii） incorporating TEFs into the inflected S-shaped NHPP SRGM may be more effective and appropriate compared with the exponential-type and the delayed S-shaped NHPP SRGMs;（iii） the inflected S-shaped NHPP SRGM considering both IS-TEF and ID yields the most accurate fitting and prediction results than the other comparison NHPP SRGMs.
基金supported by the National Natural Science Foundation of China(No.U1433116)the Aviation Science Foundation of China(No.20145752033)
文摘Because of the inevitable debugging lag,imperfect debugging process is used to replace perfect debugging process in the analysis of software reliability growth model.Considering neither testing-effort nor testing coverage can describe software reliability for imperfect debugging completely,by hybridizing testing-effort with testing coverage under imperfect debugging,this paper proposes a new model named GMW-LO-ID.Under the assumption that the number of faults is proportional to the current number of detected faults,this model combines generalized modified Weibull(GMW)testing-effort function with logistic(LO)testing coverage function,and inherits GMW's amazing flexibility and LO's high fitting precision.Furthermore,the fitting accuracy and predictive power are verified by two series of experiments and we can draw a conclusion that our model fits the actual failure data better and predicts the software future behavior better than other ten traditional models,which only consider one or two points of testing-effort,testing coverage and imperfect debugging.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61228203,61225007,and 61272157the National Science Foundation of USA under Grant Nos.CCF-1349666,CNS-1434582,CCF-1434596,CCF-1434590,CNS-1439481a Microsoft Research award
文摘In recent years, to maximize the value of software testing and analysis, we have proposed the methodology of cooperative software testing and analysis （in short as cooperative testing and analysis） to enable testing and analysis tools to cooperate with their users （in the form of tool-human cooperation）, and enable one tool to cooperate with another tool （in the form of tool-tool cooperation）. Such cooperations are motivated by the observation that a tool is typically not powerful enough to address complications in testing or analysis of complex real-world software, and the tool user or another tool may be able to help out some problems faced by the tool. To enable tool-human or tool-tool cooperation, effective mechanisms need to be developed 1） for a tool to communicate problems faced by the tool to the tool user or another tool, and 2） for the tool user or another tool to assist the tool to address the problems. Such methodology of cooperative testing and analysis forms a new research frontier on synergistic cooperations between humans and tools along with cooperations between tools and tools. This article presents recent example advances and challenges on cooperative testing and analysis.
文摘软件规模和复杂程度的不断提高,为软件质量保障带来了严峻的挑战.软件缺陷定位是一种重要的软件质量保障技术,其中基于频谱的缺陷定位(Spectrum-based Fault Localization,SFL)是应用最为广泛的软件缺陷定位技术,其通过分析语句覆盖信息矩阵计算代码语句的可疑度值,并根据可疑度值定位缺陷所在语句.然而,语句覆盖信息矩阵中存在着严重的数据冗余问题,冗余的数据极大地影响了SFL的缺陷定位性能.以Defects4J数据集中395个程序的语句覆盖信息矩阵为例,在超过一半的语句覆盖信息矩阵中有90%的语句存在与其具有相同覆盖信息的语句.特征选择是常用的数据预处理技术,通过去除冗余和不相关特征来获取原始特征集中有价值的特征子集.因此,我们将语句覆盖信息矩阵作为原始特征集,将冗余覆盖信息约简建模为特征选择问题,提出了一种基于冗余覆盖信息约简的软件缺陷定位方法(Fault Localization based on Redundant coverage information Reduction,FLRR).首先,使用特征选择技术对语句覆盖信息和测试用例执行结果组成的语句覆盖信息矩阵进行约简,得到语句覆盖信息矩阵子集;然后,使用SFL计算语句覆盖信息矩阵子集中语句的可疑度值,并根据可疑度值对语句进行降序排列,以定位缺陷语句.本文使用六种常用的特征选择技术对语句覆盖信息矩阵进行特征选择和约简,以得到语句覆盖信息矩阵子集,并使用四种典型的SFL技术对语句覆盖信息矩阵子集中的语句进行缺陷定位.为评估FLRR的缺陷定位性能,本文使用E_(inspect)@n和MRR(Mean Reciprocal Rank)评价指标在基于Defects4J的数据集上与四种典型的SFL技术进行了对比实验.实验结果表明,FLRR能够有效提升SFL的缺陷定位性能.对于E_(inspect)@n指标,当n=1时,FLRR相比DStar、Ochiai、Barinel和OP2分别多定位到23条、26条、14条和13条缺陷语句,分别增加了69.70%、76.47%、45.16%和38.24%;对于MRR指标,FLRR相比DStar、Ochiai、Barinel和OP2分别提升了20.08%、24.94%、17.45%和19.15%.
