arXiv subjects

Leila Yousefi

Publications and source records attributed to Leila Yousefi.

4 recordsLinked to original sources

arXiv2026

The Replication Assessment Problem in Software Engineering

Background: Replication studies in software engineering are increasingly common, yet their interpretation remains uncertain and inconsistent because assessments frequently rely on loosely defined or ad hoc criteria. Aim: This study aims to document how replication study outcomes are currently assessed in empirical software engineering, identify problems arising from inconsistent criteria and propose a principled framework for meaningful evaluation. Method: We conducted a systematic review of replication studies, with the search covering recent empirical software engineering replications (2021--2025). For each study, we extracted the criteria used to assess replication outcomes and analysed these for heterogeneity, logical consistency, and alignment with established statistical principles. Results: A total of 10 replication studies were located. The analysis reveals substantial heterogeneity in assessment practices, with contradictory criteria applied to similar data, limited acknowledgement of measurement uncertainty, and an absence of shared standards. We propose a principled framework grounded in statistical, methodological, and measurement considerations, and demonstrate its application through worked examples. Conclusions: Adopting consistent and transparent assessment principles would reduce ambiguity, improve comparability, and support more reliable evidence accumulation in software engineering replication research.

cs.SE↗

arXiv2026

An Audit of Machine Learning Experiments on Software Defect Prediction

Background: Machine learning algorithms are widely used to predict defect prone software components. In this literature, computational experiments are the main means of evaluation, and the credibility of results depends on experimental design and reporting. Objective: This paper audits recent software defect prediction (SDP) studies by assessing their experimental design, analysis, and reporting practices against accepted norms from statistics, machine learning, and empirical software engineering. The aim is to characterise current practice and assess the reproducibility of published results. Method: We audited SDP studies indexed in SCOPUS between 2019 and 2023, focusing on design and analysis choices such as outcome measures, out of sample validation strategies, and the use of statistical inference. Nine study issues were evaluated. Reproducibility was assessed using the instrument proposed by González Barahona and Robles. Results: The search identified approximately 1,585 SDP experiments published during the period. From these, we randomly sampled 101 papers, including 61 journal and 40 conference publications, with almost 50 percent behind paywalls. We observed substantial variation in research practice. The number of datasets ranged from 1 to 365, learners or learner variants from 1 to 34, and performance measures from 1 to 9. About 45 percent of studies applied formal statistical inference. Across the sample, we identified 427 issues, with a median of four per paper, and only one paper without issues. Reproducibility ranged from near complete to severely limited. We also identified two cases of tortured phrases and possible paper mill activity. Conclusions: Experimental design and reporting practices vary widely, and almost half of the studies provide insufficient detail to support reproduction. The audit indicates substantial scope for improvement.

cs.SE↗

arXiv2022

An analysis of retracted papers in Computer Science

Context: The retraction of research papers, for whatever reason, is a growing phenomenon. However, although retracted paper information is publicly available via publishers, it is somewhat distributed and inconsistent. Objective: The aim is to assess: (i) the extent and nature of retracted research in Computer Science (CS) (ii) the post-retraction citation behaviour of retracted works and (iii) the potential impact on systematic reviews and mapping studies. Method: We analyse the Retraction Watch database and take citation information from the Web of Science and Google scholar. Results: We find that of the 33,955 entries in the Retraction watch database (16 May 2022), 2,816 are classified as CS, i.e., approximately 8.3%. For CS, 56% of retracted papers, provide little or no information as to the reasons. This contrasts with 26% for other disciplines. There is also a remarkable disparity between different publishers, a tendency for multiple versions of a retracted paper over and above the Version of Record (VoR), and for new citations long after a paper is officially retracted. Conclusions: Unfortunately retraction seems to be a sufficiently common outcome for a scientific paper that we as a research community need to take it more seriously, e.g., standardising procedures and taxonomies across publishers and the provision of appropriate research tools. Finally, we recommend particular caution when undertaking secondary analyses and meta-analyses which are at risk of becoming contaminated by these problem primary studies.

cs.DL↗

arXiv2019

The Prevalence of Errors in Machine Learning Experiments

Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.

cs.LG↗