A processor implemented method, comprising: obtaining, via one or more hardware processors, a plurality of log files from at least one of a first execution environment and a second execution environment, wherein the plurality of log files correspond to one or more file formats ; pre-processing, via the one or more hardware processors, the plurality of log files to obtain a plurality of pre-processed log files further comprising one or more unique error sentences ; tokenizing, via the one or more hardware processors, the one or more unique error sentences by using a Natural Language Processing (NLP) library ; removing, via the one or more hardware processors, stop words comprised in the one or more unique error sentences ; calculating, via the one or more hardware processors, a frequency of each word comprised in the one or more unique error sentences ; calculating, by using a word vector model via the one or more hardware processors, a word weight for each word comprised in the one or more unique error sentences based on the calculated frequency ; generating, via the one or more hardware processors, one or more error sentence vectors for the one or more unique error sentences using the calculated word weight for each word ; computing, via the one or more hardware processors, a cosine similarity between the one or more unique error sentence vectors of the plurality of log files and a plurality of error sentence vectors comprised in a pre-defined database, by using a modified Smooth Inverse Frequency (mSIF) model to obtain a set of matched cosine similarities ; performing, via the one or more hardware processors, a comparison of each cosine similarity comprised in the set of matched cosine similarities with a pre-defined threshold value ; and identifying, via the one or more hardware processors, one or more solutions to the one or more unique error sentences based on the comparison . obtaining, via one or more hardware processors, a plurality of log files from at least one of a first execution environment and a second execution environment, wherein the plurality of log files correspond to one or more file formats ; pre-processing, via the one or more hardware processors, the plurality of log files to obtain a plurality of pre-processed log files further comprising one or more unique error sentences ; tokenizing, via the one or more hardware processors, the one or more unique error sentences by using a Natural Language Processing (NLP) library ; removing, via the one or more hardware processors, stop words comprised in the one or more unique error sentences ; calculating, via the one or more hardware processors, a frequency of each word comprised in the one or more unique error sentences ; calculating, by using a word vector model via the one or more hardware processors, a word weight for each word comprised in the one or more unique error sentences based on the calculated frequency ; generating, via the one or more hardware processors, one or more error sentence vectors for the one or more unique error sentences using the calculated word weight for each word ; computing, via the one or more hardware processors, a cosine similarity between the one or more unique error sentence vectors of the plurality of log files and a plurality of error sentence vectors comprised in a pre-defined database, by using a modified Smooth Inverse Frequency (mSIF) model to obtain a set of matched cosine similarities ; performing, via the one or more hardware processors, a comparison of each cosine similarity comprised in the set of matched cosine similarities with a pre-defined threshold value ; and identifying, via the one or more hardware processors, one or more solutions to the one or more unique error sentences based on the comparison . <EOS>