Code readability strongly influences code compre-hension and, to some degree, code quality. Unreadable code makes software maintenance more challenging and is prone to more bugs. To improve the readability, using good identifier names is crucial. Existing studies on automatic identifier re-naming have not considered aspects such as the code context. Additionally, prior research has done little to address the typical challenges inherent in the identifier renaming task. In this paper, we propose a new approach for renaming identifiers in source code by fine-tuning a transformer model. Through the use of perplexity as an evaluation metric, our results demonstrate a significant decrease in the perplexity values for the fine-tuned approach compared to the baseline, reducing them from 363 to 36. To further validate our method, we conduct a developers' survey to gauge the suitability of the generated identifiers, comparing original identifiers with identifiers generated with our approach as well as two state-of-the-art large language models, GPT-4 Turbo and Gemini Pro. Our approach generates better identifier names than the original names and exhibits competitive performance with state-of-the-art commercial large language models. The proposed method carries significant implications for software developers, tool vendors, and researchers. Software developers may use our proposed approach to generate better variable names, increasing the clarity and readability of the software. Researchers in the field may use and build upon the proposed approach for variable renaming.

Software requirement classification is becoming increasingly crucial for the industry to keep up with the demand of growing project sizes.Based on client feedback or demand, software requirement classification is critical in segregating user needs into functional and quality requirements.However, because there are numerous machine learning (ML) and deep-learning (DL) models that require parameter tuning, the use of ML to facilitate decision-making across the software engineering pipeline is not well understood.Five distinct word embedding techniques were applied to the functional and quality software requirements in this study.The imbalanced classes in the dataset are balanced using Synthetic Minority Oversampling technique (SMOTE).Then, to reduce duplicate and unnecessary features, feature selection and dimensionality reduction techniques are used.Dimensionality reduction is accomplished with Principal Component Analysis (PCA), while feature selection is accomplished with the Rank-Sum Test (RST).For binary categorization into functional and non-functional needs, the generated vectors are provided as inputs to eight distinct Deep Learning classifiers.The findings of the research show that using a combination of word embedding and feature selection techniques in conjunction with various classifiers can accurately classify functional and quality software requirements.