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 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 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.