Clone Detection Research Articles

A model-based mutation framework for IEC61131-3 manufacturing systems

Abstract This article presents a model-based mutation framework for creating mutant systems implemented in IEC61131-3 languages. The framework is based on a meta-model of the IEC61131-3 standard. It supports 21 mutation operations, such as the addition or removal of POUs or the mutation of Structured Text and Sequential Functional Charts. Moreover, operations can be combined to create higher-order changes, such as Type III code clones. The mutation framework can be used to evaluate and improve development tools for supporting the evolution of manufacturing systems, such as regression test suite generators or clone detectors.

Challenging Machine Learning-Based Clone Detectors via Semantic-Preserving Code Transformations

Software clone detection identifies similar code snippets. It has been an active research topic that attracts extensive attention over the last two decades. In recent years, machine learning (ML) based detectors, especially deep learning-based ones, have demonstrated impressive capability on clone detection. It seems that this longstanding problem has already been tamed owing to the advances in ML techniques. In this work, we would like to challenge the robustness of the recent ML-based clone detectors through code semantic-preserving transformations. We first utilize fifteen simple code transformation operators combined with commonly-used heuristics (i.e., Random Search, Genetic Algorithm, and Markov Chain Monte Carlo) to perform equivalent program transformation. Furthermore, we propose a deep reinforcement learning-based sequence generation (DRLSG) strategy to effectively guide the search process of generating clones that could escape from the detection. We then evaluate the ML-based detectors with the pairs of original and generated clones. We realize our method in a framework named CloneGen. CloneGen In evaluation, we challenge the two state-of-the-art ML-based detectors and four traditional detectors with the code clones after semantic-preserving transformations via the aid of CloneGen. Surprisingly, our experiments show that, despite the notable successes achieved by existing clone detectors, the ML models inside these detectors still cannot distinguish numerous clones produced by the code transformations in CloneGen. In addition, adversarial training of ML-based clone detectors using clones generated by CloneGen can improve their robustness and accuracy. CloneGen Meanwhile, compared with the commonly-used heuristics, the DRLSG strategy has shown the best effectiveness in generating code clones to decrease the detection accuracy of the ML-based detectors.

Ranking code clones to support maintenance activities

Ranking code clones to support maintenance activities

Improving the Detection of Epidemic Clones in Candida parapsilosis Outbreaks by Combining MALDI-TOF Mass Spectrometry and Deep Learning Approaches.

Identifying fungal clones propagated during outbreaks in hospital settings is a problem that increasingly confronts biologists. Current tools based on DNA sequencing or microsatellite analysis require specific manipulations that are difficult to implement in the context of routine diagnosis. Using deep learning to classify the mass spectra obtained during the routine identification of fungi by MALDI-TOF mass spectrometry could be of interest to differentiate isolates belonging to epidemic clones from others. As part of the management of a nosocomial outbreak due to Candida parapsilosis in two Parisian hospitals, we studied the impact of the preparation of the spectra on the performance of a deep neural network. Our purpose was to differentiate 39 otherwise fluconazole-resistant isolates belonging to a clonal subset from 56 other isolates, most of which were fluconazole-susceptible, collected during the same period and not belonging to the clonal subset. Our study carried out on spectra obtained on four different machines from isolates cultured for 24 or 48 h on three different culture media showed that each of these parameters had a significant impact on the performance of the classifier. In particular, using different culture times between learning and testing steps could lead to a collapse in the accuracy of the predictions. On the other hand, including spectra obtained after 24 and 48 h of growth during the learning step restored the good results. Finally, we showed that the deleterious effect of the device variability used for learning and testing could be largely improved by including a spectra alignment step during preprocessing before submitting them to the neural network. Taken together, these experiments show the great potential of deep learning models to identify spectra of specific clones, providing that crucial parameters are controlled during both culture and preparation steps before submitting spectra to a classifier.

Code Cloning in Smart Contracts on the Ethereum Platform: An Extended Replication Study

Smart contracts are programs deployed on blockchains that run upon meeting predetermined conditions. Once deployed, smart contracts are immutable, thus, defects in the deployed code cannot be fixed. As a consequence, software engineering anti-patterns, such as code cloning, pose a threat to code quality and security if unnoticed before deployment. In this paper, we report on the cloning practices of the Ethereum blockchain platform by analyzing 33,073 smart contracts amounting to over 4MLOC. Prior work reported an unusually high 79.2% of code clones in Ethereum smart contracts. We replicate this study at the conceptual level, i.e., we answer the same research questions by employing different methods. In particular, we analyze clones at the granularity of functions instead of code files, thereby providing a more fine-grained estimate of the clone ratio. Furthermore, we analyze more complex clone types, allowing for a richer analysis of cloning cases. To achieve this finer granularity of cloning analysis, we rely on the NiCad clone detection tool and extend it with support for Solidity, the programming language of the Ethereum platform. Our analysis shows that most findings of the original study hold at the finer granularity of our study as well; but also sheds light on some differences, and contributes new findings. Most notably, we report a 30.13% overall clone ratio, out of which 27.03% are exact duplicates. Our findings motivate improving the reuse mechanisms of Solidity, and in a broader context, of programming languages used for the development of smart contracts. Tool builders and language engineers can use this paper in the design and development of such reuse mechanisms. Business stakeholders can use this paper to better assess the security risks and technical outlooks of blockchain platforms.

Graph-based code semantics learning for efficient semantic code clone detection

Graph-based code semantics learning for efficient semantic code clone detection

SSDTutor: A feedback-driven intelligent tutoring system for secure software development

Application Programming Interfaces (APIs) in cryptography typically impose concealed usage constraints. The violations of these usage constraints can lead to software crashes or security vulnerabilities. Several professional tools can detect these constraints (API misuses) in cryptography; however, in the educational programs, the focus has been less on helping students implement an application without cryptographic API misuses that are caused by either a lack of cryptographic knowledge or programming mistakes. To address the problem, we present an intelligent tutoring approach SSDTutor for educating Secure Software Development. Our tutoring approach helps students or developers repair cryptographic API misuse defects by leveraging an automated program repair technique based on the usage patterns of cryptographic APIs. We studied the best practices of cryptographic implementations and encoded eight cryptographic API usage patterns. For quality feedback, we leverage a clone detection technique to recommend related feedback for helping students understand why their programs are incorrect, rather than blindly accepting repairs. We evaluated SSDTutor on 456 open source subject projects implemented with cryptographic APIs. SSDTutor successfully detected 1,553 out of 1,573 misuse defects with 98.9% accuracy and repaired 1,551 out of 1,573 misuse defects with 99.3% accuracy. In a user study involving 22 students, the participants reported that interactive SSDTutor's feedback recommendation could be valuable for novice students to learn about the correct usages of cryptography APIs.

DupHunter: Detecting Duplicate Pull Requests in Fork-Based Development

The emergence of numerous fork-based development platforms facilitates the development of Open-Source Software (OSS) projects. Developers across the world can fork software projects and submit their Pull Requests (PRs) to the projects. However, as the number of forks increases, numerous duplicate PRs might be submitted. These duplicate PRs may cause extra code review workload and frustrate developers working on the projects. To detect duplicate PRs, many approaches have been proposed, which analyze the similarity of different elements in PRs. However, previous approaches still suffer from unsatisfied detection accuracy due to two challenges. That is, they ignore the syntactic structural information of text elements in PRs and lack the joint reasoning between different elements of two PRs. In this study, we propose an automated duplicate PRs detector named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DupHunter</i> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dup</b> licate PRs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hunter</i> ), which includes a graph embedding component and a duplicate PRs detection component to address the above challenges. The graph embedding component uses a feature graph to represent a PR. It encodes the syntactic structure and semantics of text elements (e.g., the title and the description), as well as the knowledge of non-text elements (e.g., the submission time), to address the syntactic structural information challenge. The duplicate PRs detection component tackles the joint reasoning challenge using a graph matching network, which enables the information exchange and matching across different elements of two feature graphs with an attention coefficient mechanism. Experiments on 26 open-source projects show that DupHunter achieves an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1-score@1</i> value of 0.650, significantly outperforming the state-of-the-art approaches by 3.2% to 48.1%. DupHunter can accurately detect duplicate PRs, with an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Precision@1</i> value of 0.922 and an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recall@1</i> value of 0.502.

Clonal hematopoiesis of indeterminate potential and clonal cytopenias of undetermined significance: 2023 update on clinical associations and management recommendations.

Clonal hematopoiesis (CH) refers to age-associated expansion of somatic variants in hematopoietic stem and progenitor cells (HSPC). CH of indeterminate potential (CHIP) is operationally defined as pathogenic variants in HSPCs at a variant allele frequency ≥2%. CH is associated with increased occurrence of several hematological conditions such as cytopenias (also called clonal cytopenia of undetermined significance), hematological (predominantly myeloid but also lymphoid) neoplasms, cytosis (including monocytosis), and non-hematological conditions such as atherosclerotic cardiovascular and cerebrovascular disease, ischemic congestive heart failure, venous thromboembolism, type 2 diabetes mellitus, chronic obstructive pulmonary disease, osteoporosis, gout, with a potential protective effect in Alzheimer's disease (AD). As of now, there is limited prospective data for CH testing; however, CH detection is becoming increasingly prevalent due to ubiquitous use of somatic and germline NGS testing. This in addition to data suggesting that therapy related myeloid neoplasm (tMN) in many cases is preceded by the detection of CH clones, has led to the establishment of CH clinics at several institutions. At our institution, on a research basis, we currently recommend testing for CH for individuals with persistent (≥4 months) unexplained cytopenias, in patients with malignancies prior to adjuvant cytotoxic chemotherapy and/or radiation or radionuclide therapy, screening prior to autologous hematopoietic stem cell transplantation and chimeric antigen receptor T cell (CAR-T) therapy and to assess as to whether or not, potential germline mosaic variants actually represent CH.

Towards Learning Generalizable Code Embeddings Using Task-agnostic Graph Convolutional Networks

Code embeddings have seen increasing applications in software engineering (SE) research and practice recently. Despite the advances in embedding techniques applied in SE research, one of the main challenges is their generalizability. A recent study finds that code embeddings may not be readily leveraged for the downstream tasks that the embeddings are not particularly trained for. Therefore, in this article, we propose GraphCodeVec , which represents the source code as graphs and leverages the Graph Convolutional Networks to learn more generalizable code embeddings in a task-agnostic manner. The edges in the graph representation are automatically constructed from the paths in the abstract syntax trees, and the nodes from the tokens in the source code. To evaluate the effectiveness of GraphCodeVec , we consider three downstream benchmark tasks (i.e., code comment generation, code authorship identification, and code clones detection) that are used in a prior benchmarking of code embeddings and add three new downstream tasks (i.e., source code classification, logging statements prediction, and software defect prediction), resulting in a total of six downstream tasks that are considered in our evaluation. For each downstream task, we apply the embeddings learned by GraphCodeVec and the embeddings learned from four baseline approaches and compare their respective performance. We find that GraphCodeVec outperforms all the baselines in five out of the six downstream tasks, and its performance is relatively stable across different tasks and datasets. In addition, we perform ablation experiments to understand the impacts of the training context (i.e., the graph context extracted from the abstract syntax trees) and the training model (i.e., the Graph Convolutional Networks) on the effectiveness of the generated embeddings. The results show that both the graph context and the Graph Convolutional Networks can benefit GraphCodeVec in producing high-quality embeddings for the downstream tasks, while the improvement by Graph Convolutional Networks is more robust across different downstream tasks and datasets. Our findings suggest that future research and practice may consider using graph-based deep learning methods to capture the structural information of the source code for SE tasks.

Clonal hematopoiesis detection in patients with cancer using cell-free DNA sequencing

In the context of cancer, clonal hematopoiesis of indeterminate potential (CHIP) is associated with the development of therapy-related myeloid neoplasms and shorter overall survival. Cell-free DNA (cfDNA) sequencing is becoming widely adopted for genomic screening of patients with cancer but has not been used extensively to determine CHIP status because of a requirement for matched blood and tumor sequencing. We present an accurate classification approach to determine the CH status from cfDNA sequencing alone, applying our model to 4324 oncology clinical cfDNA samples. Using this method, we determined that 30.3% of patients in this cohort have evidence of CH, and the incidence of CH varies by tumor type. Matched RNA sequencing data show evidence of increased inflammation, especially neutrophil activation, within the tumors and tumor microenvironments of patients with CH. In addition, patients with CH had evidence of neutrophil activation systemically, pointing to a potential mechanism of action for the worse outcomes associated with CH status. Neutrophil activation may be one of many mechanisms, however, because patients with estrogen receptor-positive breast cancer harboring TET2 frameshift mutations had worse outcomes but similar neutrophil frequencies to patients without CH. Together, these data show the feasibility of detecting CH through cfDNA sequencing alone and an application of this method, demonstrating increased inflammation in patients with CH both systemically and in the tumor microenvironment.

Cross-project clone consistent-defect prediction via transfer-learning method

Code clones are comparable code snippets that are introduced into software by developers in order to increase software development productivity. A change to code clone may result in a consistent-defect if the developers forget to verify the consistency of the code after the change. To reduce such change-related maintenance costs, researchers have proposed a number of methods for predicting clone consistency in advance. Unfortunately, the effectiveness of these cross-project models is unsatisfactory, and performing such predictions with insufficient data remains a challenge. Meanwhile, cross-project defect prediction via transfer learning method is prevalent in the software engineering community. Consequently, we first construct an empirical study to explore whether transfer-learning techniques could well be utilized for clone cross-project consistent-defect prediction in the initial stages of software development. In this paper, we employ transfer-learning techniques to predict clone consistency at both the time of clone creating and clone changing in order to avoid clone consistent-defects and maintenance. We conduct an experiment on open-source projects to evaluate the effectiveness of various transfer-learning methods. Our investigation demonstrates that transfer-learning techniques have a beneficial impact on predicting cross-project clone consistent-defect, and that the size of the dataset also has a positive effect on prediction. In order to promote software safety and security, we recommend that developers leverage transfer-learning to enhance the capability for clone cross-project consistent-defect prediction early in the software development phase.

Predicting the change propagation of code clones at a pull request level

Predicting the change propagation of code clones at a pull request level

AmpSeqR: an R package for amplicon deep sequencing data analysis

Amplicon sequencing (AmpSeq) is a methodology that targets specific genomic regions of interest for polymerase chain reaction (PCR) amplification so that they can be sequenced to a high depth of coverage. Amplicons are typically chosen to be highly polymorphic, usually with several highly informative, high frequency single nucleotide polymorphisms (SNPs) segregating in an amplicon of 100–200 base pair (bp). This allows high sensitivity detection and quantification of the frequency of each sequence within each sample making it suitable for applications such as low frequency somatic mosaicism detection or minor clone detection in mixed samples. AmpSeq is being increasingly applied to both biological and medical studies, in applications such as cancer, infectious diseases and brain mosaicism studies. Current bioinformatics pipelines for AmpSeq data processing lack downstream analysis, have difficulty distinguishing between true sequences and PCR sequencing errors and artifacts, and often require bioinformatic expertise. We present a new R package: AmpSeqR, designed for the processing of deep short-read amplicon sequencing data, with a focus on infectious diseases. The pipeline integrates several existing R packages combining them with newly developed functions to perform optimal filtering of reads to remove noise and improve the accuracy of the detected sequences data, permitting detection of very low frequency clones in mixed samples. The package provides useful functions including data pre-processing, amplicon sequence variants (ASVs) estimation, data post-processing, data visualization, and automatically generates a comprehensive Rmarkdown report that contains all essential results facilitating easy inclusion into reports and publications. AmpSeqR is publicly available at https://github.com/bahlolab/AmpSeqR.

Predicting the change propagation of code clones at a pull request level

Predicting the change propagation of code clones at a pull request level

Predicting the change propagation of code clones at a pull request level

Predicting the change propagation of code clones at a pull request level

SimCoDe-NET: Similarity Detection in Binary Code using Deep Learning Network

Binary code similarity detection is a fundamental task in the field of computer binary security. However, code similarity is crucial today because of the prevalence of issues like plagiarism, code cloning, and recycling in software due to the ongoing increase of software scale. To resolve these issues, a novel SIMilarity detection in binary COde using DEep learning NETwork (SimCoDe-NET) has been proposed. Initially, op-code features are extracted from the input data by using reverse engineering process and the opcode embedding is generated using N-skip gram method. The extracted features are fed into Bi-GRU neural network for classifying the similarity of the binary codes. The Bi-GRU neural network compares two data samples in feature space to identify whether they belong to similar data or non-similar data. The SimCoDe-NET framework is evaluated by using generated dataset to assess the efficiency of this method. The efficacy of the proposed SimCoDe-NET framework is assessed in terms of precision, accuracy, sensitivity, recall, similarity detection time and similarity detection rate. The accuracy of the proposed method is 99.10% which is relatively high compared to the existing method. The proposed SimCoDe-NET approach improves the accuracy by 84.9%, 88.58%, and 93.9% better than jTrans, UPPC, and HEBCS respectively.

Combining MYD88 L265P mutation detection and clonality determination on CSF cellular and cell-free DNA improves diagnosis of primary CNS lymphoma.

Diagnosis of primary central nervous system lymphoma (PCNSL) is challenging, and although brain biopsy remains the gold standard, cerebrospinal fluid (CSF) constitutes a less invasive source of lymphomatous biomarkers. In a retrospective cohort of 54 PCNSL cases tested at diagnosis or relapse, we evaluated the contribution of immunoglobulin heavy chain (IGH) gene clonality and MYD88 L265P detection on both CSF cell pellets and supernatants, in comparison with cytology, flow cytometry, interleukin (IL)-10 and IL-6 quantification. Clonality assessment included a new assay to detect partial IGH-DJ rearrangements. Clonal IGH rearrangements and/or MYD88 L265P mutation were detected in 27 (50%) cell pellets and 24 (44%) supernatant cell-free (cf) DNA. Combining analyses on both compartments, 36 (66%) cases had at least one detectable molecular marker, present only in cfDNA for 9 (16%) of them. While cytology and flow cytometry were positive in only 7 (13.0%) and 9 (17.3%) cases respectively, high IL-10 levels were observed in 36 (66.7%) cases. Overall, taking into account molecular and cytokine results, 46/54 (85%) cases had at least one lymphomatous biomarker detectable in the CSF. These results show that this combination of biomarkers evaluated on both cell pellet and supernatant CSF fractions improves significantly the biological diagnosis of PCNSL.

Clone consistent-defect prediction based on deep learning method

Many consistent changes take place across code clones in software, and this has emerged as a severe issue for software security. If the developers forget to modify the relevant clones consistently, such modifications will introduce consistent-defect. Researchers leverage machine learning techniques to predict clone consistent-defect by representing these changes with the designed attributes. Meanwhile, deep learning technology has demonstrated tremendous potential in characterizing source code. As such, we explore whether deep learning technology can enhance the effectiveness of clone consistent-defect prediction. In this study, we investigate various neural networks for modeling clone consistent change. Specifically, our approach models code clones and their evolution to capture the semantic properties automatically from the perspectives of clone fragment, clone group, and clone evolution, as opposed to manually generated attributes. To evaluate the effectiveness of our approach, we conduct an experiment on the dataset collected from 8 open-source projects. The results demonstrate that our neural network models are efficient both in cross-project and within-project scenarios, with F-measures of around 80% and recalls of around 90%. We conclude that deep learning technology may successfully assist developers in predicting clone consistent-defect, so helping to improve the security of code clones by alerting developers to confirm their consistency.

A Novel Source Code Clone Detection Method Based on Dual-GCN and IVHFS

Source code clone detection, which can identify code fragments with similar functions, plays a significant role in software development and quality assurance. Existing methods either extract single syntactic or semantic information, or ignore the associated information between code statements in different structures. It is difficult for these methods to effectively detect clone pairs with similar functions. In this paper, we propose a new model based on a dual graph convolutional network (GCN) and interval-valued hesitant fuzzy set (IVHFS), which we named DG-IVHFS. Specifically, we simplified and grouped the abstract syntax tree (AST) of source code to obtain the group representations. The group representations of the AST, as well as the control flow graph (CFG) representations, were transformed into graph structures, and then we applied GCNs on them to learn dependencies between nodes. In addition, we introduced IVHFS into the model for a more comprehensive evaluation of similarity. Our experimental results demonstrated that the precision, recall, and F1-scores of DG-IVHFS on the BigCloneBench and GoogleCodeJam datasets reached 98, 97 and 97% and 98, 93 and 95%, respectively, exceeding current state-of-the-art models. Moreover, our model performed well in terms of time consumption.