Self-learning Change-prone Class Prediction

Yan, Meng; Yang, Mengning; Liu, Chao; Zhang, Xiaohong

doi:10.18293/seke2016-039

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…The popularity of DL models in SE is mainly due to the advantages of representation learning from raw data [79,95,134]. For example, in many recent SE studies, a large number of challenges derive from the semantic comprehension of code in programming languages [84,85,148,149,154], text in natural languages [34,150], or their mutual transformation [44]. As code and text involves some form of natural language processing (NLP), it commonly starts with encoding words by a fixed size of vocabulary [44].…”

Section: Background and Related Work 21 DL Technology In Sementioning

confidence: 99%

On the Replicability and Reproducibility of Deep Learning in Software Engineering

Liu,

Gao,

Xia

et al. 2020

Preprint

Self Cite

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Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) replicability -whether the reported experimental result can be approximately reproduced in high probability with the same DL model and the same data; and (2) reproducibility -whether one reported experimental findings can be reproduced by new experiments with the same experimental protocol and DL model, but different sampled real-world data. Unlike traditional machine learning (ML) models, DL studies commonly overlook these two factors and declare them as minor threats or leave them for future work. This is mainly due to high model complexity with many manually set parameters and the time-consuming optimization process. In this study, we conducted a literature review on 93 DL studies recently published in twenty SE journals or conferences. Our statistics show the urgency of investigating these two factors in SE, where only 10.8% of the studies discussed any research questions affecting replicability and/or reproducibility. More than 74.2% of the studies do not even share source code and data to support the replicability of their complex models. Moreover, we re-ran four representative DL models in SE. Experimental results show the importance of replicability and reproducibility, where the reported performance of a DL model could not be replicated for an unstable optimization process. Reproducibility could be substantially compromised if the model training is not convergent, or if performance is sensitive to the size of vocabulary and testing data. It is therefore urgent for the SE community to provide a long-lasting link to a replication package, enhance DL-based solution stability and convergence, and avoid performance sensitivity on different sampled data.CCS Concepts: • Software and its engineering → Software maintenance tools.

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Section: Background and Related Work 21 DL Technology In Sementioning

confidence: 99%

On the Replicability and Reproducibility of Deep Learning in Software Engineering

Liu,

Gao,

Xia

et al. 2020

Preprint

Self Cite

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“…In the last decade, machine learning (ML) techniques have been applied to defect prediction [4] with various approaches, such as Bayesian networks, neural networks, multivariate regression and ensemble methods [5]. Nevertheless, the construction of a SDP model is a challenging activity that has seen researches propose different solutions [6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Software defect prediction: A study on software metrics using statistical and machine learning methods

Canaparo¹,

Ronchieri²,

Bertaccini³

2022

Proceedings of International Symposium on Grids &Amp; Clouds 2022 — PoS(ISGC2022)

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Software defect prediction aims at identifying defect prone software modules in order to allocate optimal testing resources. The role of testing in software development life cycle is vital especially when software systems are becoming more and more complex, representing a suitable environment for defects. Several researchers have striven to develop models able to determine defective modules with the aim of reducing time and cost of software testing. Such models are typically trained on software measurements, known as software metrics, which describe the characteristics of a software project in terms of e.g., dimension and complexity. These metrics reduce the subjectivity of software quality assessment and can be relied on for decision making, e.g., to decide where to focus software tests. The aim of our work is to employ both feature selection or construction techniques and machine learning techniques to build software defect prediction models on different kinds of software dataset metrics (derived from various software projects available in the NASA and Eclipse repositories), and assess their performances by considering accuracy, precision, recall and area under the curve. We have used non parametric tests to compute the statistical significance of the obtained results. The collected metrics belong to three main categories: dimension, complexity and object orientation. The involved datasets contain class labels, i.e., information on the defectiveness of the software modules. To make our study available to research community, we have developed an open source and extensible R application that supports researchers to load the selected kinds of datasets, to filter them according to the their features and to apply machine learning techniques.

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