arXiv subjects

Manal Binkhonain

Publications and source records attributed to Manal Binkhonain.

4 recordsLinked to original sources

arXiv2026

A systematic literature review on logging smell detection

Context:Logging is an important part of software development that helps developers monitor systems, understand behavior, and fix problems. But when logging is done poorly, it can introduce logging smells, which are defects that reduce the usefulness of logs or even make them problematic. Objective:This study looks at how logging smells are currently detected. The goal is to better understand the existing research on automatic detection techniques, datasets, and evaluation methods. Method:We conducted a systematic literature review (SLR) of 21 studies focused on detecting logging smells. In this review, we define key logging-related terms, identify and map the types of smells to an existing taxonomy, and examine the detection techniques, datasets, and evaluation strategies used across the studies. Results:We found that the research is still scattered and inconsistent. For example, there is no common benchmark or standardized approach for evaluating results, making it difficult to compare studies. In addition, we observe inconsistencies in the way log smells are addressed, as studies differ in the types and number of smells they target. Conclusion:There is still room for improvement in how logging smells are studied and detected. We point out several challenges and suggest future directions, such as developing better tools, using large language models (LLMs), and building more standardized datasets for evaluation.

cs.SE↗

arXiv2026

Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?

Sentiment analysis in software engineering focuses on understanding emotions expressed in software artifacts. Previous research highlighted the limitations of applying general off-the-shelf sentiment analysis tools within the software engineering domain and indicated the need for specialized tools tailored to various software engineering contexts. The development of such tools heavily relies on supervised machine learning techniques that necessitate annotated datasets. Acquiring such datasets is a substantial challenge, as it requires domain-specific expertise and significant effort. Objective: This study explores the potential of ZSL to address the scarcity of annotated datasets in sentiment analysis within software engineering Method:} We conducted an empirical experiment to evaluate the performance of various ZSL techniques, including embedding-based, NLI-based, TARS-based, and generative-based ZSL techniques. We assessed the performance of these techniques under different labels setups to examine the impact of label configurations. Additionally, we compared the results of the ZSL techniques with state-of-the-art fine-tuned transformer-based models. Finally, we performed an error analysis to identify the primary causes of misclassifications. Results: Our findings demonstrate that ZSL techniques, particularly those combining expert-curated labels with embedding-based or generative-based models, can achieve macro-F1 scores comparable to fine-tuned transformer-based models. The error analysis revealed that subjectivity in annotation and polar facts are the main contributors to ZSL misclassifications. Conclusion: This study demonstrates the potential of ZSL for sentiment analysis in software engineering. ZSL can provide a solution to the challenge of annotated dataset scarcity by reducing reliance on annotated dataset.

cs.SE↗

arXiv2025

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification

Requirements classification assigns natural language requirements to predefined classes, such as functional and non functional. Accurate classification reduces risk and improves software quality. Most existing models rely on supervised learning, which needs large labeled data that are costly, slow to create, and domain dependent; they also generalize poorly and often require retraining for each task. This study tests whether prompt based large language models can reduce data needs. We benchmark several models and prompting styles (zero shot, few shot, persona, and chain of thought) across multiple tasks on two English datasets, PROMISE and SecReq. For each task we compare model prompt configurations and then compare the best LLM setups with a strong fine tuned transformer baseline. Results show that prompt based LLMs, especially with few shot prompts, can match or exceed the baseline. Adding a persona, or persona plus chain of thought, can yield further gains. We conclude that prompt based LLMs are a practical and scalable option that reduces dependence on large annotations and can improve generalizability across tasks.

cs.SE↗

arXiv2023

A Machine Learning Approach for Hierarchical Classification of Software Requirements

Context: Classification of software requirements into different categories is a critically important task in requirements engineering (RE). Developing machine learning (ML) approaches for requirements classification has attracted great interest in the RE community since the 2000s. Objective: This paper aims to address two related problems that have been challenging real-world applications of ML approaches: the problems of class imbalance and high dimensionality with low sample size data (HDLSS). These problems can greatly degrade the classification performance of ML methods. Method: The paper proposes HC4RC, a novel ML approach for multiclass classification of requirements. HC4RC solves the aforementioned problems through semantic-role-based feature selection, dataset decomposition and hierarchical classification. We experimentally compare the effectiveness of HC4RC with three closely related approaches - two of which are based on a traditional statistical classification model whereas one uses an advanced deep learning model. Results: Our experiment shows: 1) The class imbalance and HDLSS problems present a challenge to both traditional and advanced ML approaches. 2) The HC4RC approach is simple to use and can effectively address the class imbalance and HDLSS problems compared to similar approaches. Conclusion: This paper makes an important practical contribution to addressing the class imbalance and HDLSS problems in multiclass classification of software requirements.

cs.SE↗