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This dissertation presents an evaluation of several language models onsoftware defect datasets. A language Model (LM) "can provide wordrepresentation and probability indication of word sequences as the corecomponent of an NLP system." Language models for source code are specified fortasks in the software engineering field. While some models are directly the NLPones, others contain structural information that is uniquely owned by sourcecode. Software defects are defects in the source code that lead to unexpectedbehaviours and malfunctions at all levels. This study provides an originalattempt to detect these defects at three different levels (syntactical,algorithmic and general) We also provide a tool chain that researchers can useto reproduce the experiments. We have tested the different models againstdifferent datasets, and performed an analysis over the results. Our originalattempt to deploy bert, the state-of-the-art model for multitasks, leveled oroutscored all other models compared.

An Evalutation of Programming Language Models' performance on Software Defect Detection