Clone Detection Research Articles

Prioritisation of code clones using a genetic algorithm

Context:Code clones are prevalent, and due to their diverse impact on projects’ quality they require a proper management strategy. Objectives:Develop GA-based Refactoring-Aware Detection (RAD) approach for prioritisation of code clones. Method:A genetic algorithm (GA) that balances estimated gain and cost/risk of refactoring to select the optimal clone candidate to refactor. Results:GA converges on a solution, with diverse variance. The value of fitness function is higher for multi-objective approaches, but they also exhibit higher variance. Conclusion:GA can be effectively applied for clone prioritising.

Poison Attack and Poison Detection on Deep Source Code Processing Models

In the software engineering (SE) community, deep learning (DL) has recently been applied to many source code processing tasks, achieving state-of-the-art results. Due to the poor interpretability of DL models, their security vulnerabilities require scrutiny. Recently, researchers have identified an emergent security threat to DL models, namely, poison attacks . The attackers aim to inject insidious backdoors into DL models by poisoning the training data with poison samples. The backdoors mean that poisoned models work normally with clean inputs but produce targeted erroneous results with inputs embedded with specific triggers. By using triggers to activate backdoors, attackers can manipulate poisoned models in security-related scenarios (e.g., defect detection) and lead to severe consequences. To verify the vulnerability of deep source code processing models to poison attacks, we present a poison attack approach for source code named CodePoisoner as a strong imaginary enemy. CodePoisoner can produce compilable and functionality-preserving poison samples and effectively attack deep source code processing models by poisoning the training data with poison samples. To defend against poison attacks, we further propose an effective poison detection approach named CodeDetector . CodeDetector can automatically identify poison samples in the training data. We apply CodePoisoner and CodeDetector to six deep source code processing models, including defect detection, clone detection, and code repair models. The results show that ❶ CodePoisoner conducts successful poison attacks with a high attack success rate (average: 98.3%, maximum: 100%). It validates that existing deep source code processing models have a strong vulnerability to poison attacks. ❷ CodeDetector effectively defends against multiple poison attack approaches by detecting (maximum: 100%) poison samples in the training data. We hope this work can help SE researchers and practitioners notice poison attacks and inspire the design of more advanced defense techniques.

FUME-TCRseq Enables Sensitive and Accurate Sequencing of the T-cell Receptor from Limited Input of Degraded RNA.

FUME-TCRseq is a TCR sequencing methodology that supports sensitive and spatially resolved detection of TCR clones in archival clinical specimens, which can facilitate longitudinal tracking of immune responses through disease course and treatment.

A new thread-level speculative automatic parallelization model and library based on duplicate code execution

Loop-efficient automatic parallelization has become increasingly relevant due to the growing number of cores in current processors and the programming effort needed to parallelize codes in these systems efficiently. However, automatic tools fail to extract all the available parallelism in irregular loops with indirections, race conditions or potential data dependency violations, among many other possible causes. One of the successful ways to automatically parallelize these loops is the use of speculative parallelization techniques. This paper presents a new model and the corresponding C++ library that supports the speculative automatic parallelization of loops in shared memory systems, seeking competitive performance and scalability while keeping user effort to a minimum. The primary speculative strategy consists of redundantly executing chunks of loop iterations in a duplicate fashion. Namely, each chunk is executed speculatively in parallel to obtain results as soon as possible and sequentially in a different thread to validate the speculative results. The implementation uses C++11 threads and it makes intensive use of templates and advanced multithreading techniques. An evaluation based on various benchmarks confirms that our proposal provides a competitive level of performance and scalability.

Expert consensus on clonal screening and monitoring of complement inhibitor therapy in paroxysmal nocturnal hemoglobinuria (2024)

Paroxysmal nocturnal hemoglobinuria (PNH) is a rare clonal disease with abnormal hematopoietic stem cells that causes intravascular hemolytic anemia, thrombosis, and peripheral blood cytopenia. It has a chronic progressive course and can be fatal in severe cases if not treated aggressively. Complement inhibitors are the first-line recommended treatment for hemolysis-related symptoms of PNH. With the rapid development of new complement inhibitors, it is critical to quickly screen and confirm the diagnosis, identify patients with complement inhibitor indications, and monitor breakthrough hemolysis and extravascular hemolysis during complement inhibitor therapy. Drawing on the most recent guidelines, works of literature, and meta-reviews from around the world, as well as combining with experience from the experts, this consensus focused on PNH screening principles, the significance of PNH cloning detection, and post-treatment monitoring of terminal complement inhibitors, which may contribute to a better understanding of diagnosis and treatment monitoring in the era of complement inhibitors.

TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member’s competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers’ views, and the method ignored the temporal aspect of open-source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state-of-the-art team recommendation algorithms using three popular open-source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state-of-the-art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.

GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

AbstractCode clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre‐defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement‐level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real‐world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state‐of‐the‐art methods.

A Novel Single-Tube Next Generation Sequencing Assay for B-Cell Receptor Clonality Testing

B-cell neoplasms possess clonal B-cell receptor rearrangements (BCR clonotype lineages) that can be identified by sequencing the B-cell repertoire for use in diagnostics, risk stratification, and high-sensitivity monitoring. BCR somatic hypermutation (SHM) can result in clonality detection failure from point mutations in PCR primer binding regions, often necessitating splitting samples into multiple reactions which increases test costs, turnaround times, and sample requirements. We evaluated the Oncomine BCR Pan-Clonality Assay, a novel single-tube PCR reaction that simultaneously amplifies all BCR loci for next-generation DNA sequencing, using neoplastic B-cell lines and clinical research samples from multiple myeloma (MM) patients, a plasma cell neoplasm associated with high SHM levels. The assay showed a linear detection range down to 1 ng of clonal DNA input, sensitivity to 10−6 in a polyclonal background, and high reproducibility. Clonotype lineages were identified in 42/45 (93%) MM samples. Ion Reporter software packaged with the assay permitted straightforward identification of MM subgroups. As expected, SHM was identified in 94% of MM cases, but several unexpected subgroups were identified including biased IGHV3-11 or IGHV4-34 usage in 20% of MM samples, and two cases with very low levels of SHM. Evidence of intraclonal diversity/ongoing SHM was identified in 18% of samples, suggesting a possible germinal center origin for some MM cases. The single-tube Oncomine BCR Pan-Clonality assay efficiently detects BCR clonotype lineages at rates comparable to existing multiple reaction assays and permits their characterization for cell of origin studies and lymphoma classification.

Federated Learning for Software Engineering: A Case Study of Code Clone Detection and Defect Prediction

Federated Learning for Software Engineering: A Case Study of Code Clone Detection and Defect Prediction

Plasma cell-free DNA in canine lymphoma patients as a novel material for genotyping.

Canine lymphoma is a disease with high morbidity and poor long-term prognosis, despite a high response rate to chemotherapy. In this study, we focused on liquid biopsy, in which small amounts of substances from body fluids were analysed, to determine whether cell-free DNA (cfDNA) in the plasma can be used as a biomarker for lymphoma in dogs. We found that 23 patients with lymphoma had significantly higher cfDNA concentrations than the 12 healthy dogs (median 2360 ng/mL versus 299 ng/mL, p < .0001). Polymerase chain reaction for antigen receptor rearrangement (PARR) was also employed using cfDNA from the lymphoma group to investigate whether cfDNA could be used for the detection of genetic clonality of lymphomas, as well as the genomic DNA (gDNA) extracted from an original lesion in each case. The correlation of the PARR results between cfDNA and gDNA was observed in 100% of B-cell lymphomas (10/10), 77.8% of T-cell lymphomas (7/9), and 100% of other types of lymphomas (4/4), respectively. These results indicate that plasma cfDNA levels are increasing in canine lymphoma patients, that cfDNA concentration can be a novel diagnostic tool, and that it can be used as a diagnostic tool for PARR.

An empirical study of code reuse between GitHub and stack overflow during software development

With the rise of programming Q&A websites (e.g., Stack Overflow) and the open-source movement, code reuse has become a common phenomenon. Our study aims to provide a comprehensive study of the code reuse behavior of programmers during software development, i.e., we mainly focus on the code reuse between the code snippets in the commits of open-source projects and the code snippets on Stack Overflow (SO). The open-source java project code dataset we construct contains 793 projects which include 342,148 modified code snippets, and the SO code dataset includes 1,355,617 posts. Then, we employ a code clone detection tool to identify the instances of code reuse between the modified code snippets of commits and the code snippets of the SO posts. We find that the average code reuse ratio of the projects is 6.32%, which will have a significant upward trend in the future. Additionally, we find that experienced developers seem to be more likely to reuse the code on SO, and prefer to reuse posts with more favorites and higher scores. We combine deep learning and topic analysis algorithms to fully exploit the semantic information of SO posts. The result shows a certain difference in the distribution of post types reused by bug-related commits and non-bug-related commits. We also discover that the code reuse ratio (14.44%) in java class files that have undergone multiple modifications is more than double the overall code reuse ratio (6.32%). Finally, we discuss the reuse habits of programmers and find that they may refer to multiple posts in one reuse, and these posts are related to a certain extent. From these results, our study provides multiple practical insights for different stakeholders: researchers, developers, and the SO platform.

PyExoCross: a Python program for generating spectra and cross-sections from molecular line lists

Abstract PyExoCross is a Python adaptation of the ExoCross Fortran application, PyExoCross is designed for post-processing the huge molecular line lists generated by the ExoMol project and other similar initiatives such as the HITRAN and HITEMP data bases. PyExoCross generates absorption and emission stick spectra, cross-sections, and other properties (partition functions, specific heats, cooling functions, lifetimes, and oscillator strengths) based on molecular line lists. PyExoCross calculates cross-sections with four line profiles: Doppler, Gaussian, Lorentzian, and Voigt profiles in both sampling and binned methods; a number of options are available for computing Voigt profiles which we test for speed and accuracy. PyExoCross supports importing and exporting line lists in the ExoMol and HITRAN/HITEMP formats. PyExoCross also provides conversion between the ExoMol and HITRAN data formats. In addition, PyExoCross has extra code for users to automate the batch download of line list files from the ExoMol data base.

Code Clone Detection Based on Bytecode and Twin Neural Networks

Abstract In order to perform code clone detection in missing source code scenarios while ensuring the code clone detection effect, this paper proposes a code clone detection method based on bytecode and twin neural networks. The process begins by extracting the function’s opcode sequence from the bytecode instruction file. Then, the opcodes are vectorized using a neural network pre-training model to ensure that they contain semantic information. Then, a twin neural network is constructed based on GRU to compute the similarity between the vector sequences. The Opcode21K dataset dedicated to bytecode is used to test the constructed model. A total of 5818611 real clone pairs and 279112 fake clone pairs are detected, and the clone pairs that have been labeled by Opcode21K are plotted on the ROC curve so as to select the distance value of 0.7 as the code clone detection threshold. The number of clone pairs detected by SJBCD, the accuracy, and the recall rate are much higher than those of most existing methods. The number of large-difference code clones detected ranges from about 20% to 50% of the total clones. Additionally, the method’s runtime is the shortest for datasets with code lines ranging from 1M to 30M in size, and the detection time for a 250M dataset is approximately 54.5 hours. Therefore, the algorithm constructed in this study can take into account the detection of code clones in a variety of situations so that the efficiency of software development can be effectively improved.

The ethanolic extract of Gomphrena celosioides is not carcinogenic and has antigenotoxic effects and chemopreventive Properties.

Gomphrena celosioides, popularly known as perpétua, perpétua brava, bachelor´s button and prostate globe amarahth, is used for the treatment of urinary tract disorders, kidney stones, for skin diseases, infectious diseases, gastrointestinal and respiratory conditions. Rich in phenolic acids and flavonoids, this plant has therefore a potential for use in cancer prevention. Given the above, the present research aimed to evaluate the carcinogenic effect of the ethanolic extract of G. celosioides (EEGc) in an alternative model of Drosophila melanogaster and the genotoxic and antigenotoxic effects in Swiss mice. The larval survival test and the detection of epithelial tumor clones were performed in D. melanogaster. The tested EEGc concentrations were 0.96, 1.92, 3.85 and 7.70 mg/mL. In Swiss mice, the genotoxicity and antigenotoxicity of doses of 100, 1,000 and 2,000 mg/Kg were evaluated. The results showed that EEGc at a concentration of 7.70 mg/mL reduced (p<0.05) larval survival. However, EEGc was not carcinogenic, and the lowest concentration (0.96 mg/mL) prevented (p<0.05) the basal occurrence of epithelial tumors. In mice, EEGc at the highest dose (2,000mg/Kg) increased the frequency of genomic lesions (p<0.05). Yet, none of the doses caused chromosomal lesions (p>0.05). When associated with cyclophosphamide, EEGc was antigenotoxic (p<0.05). The percentages of reduction of genomic damage ranged from 33.39 to 63.23% and of chromosomal damage from 20.00 to 77.19%. In view of the above, it is suggested that EEGc is not carcinogenic, has an antigenotoxic effect and chemopreventive properties.

A Novel Method for Code Clone Detection Based on Minimally Random Kernel Convolutional Transform

A Novel Method for Code Clone Detection Based on Minimally Random Kernel Convolutional Transform

Review of the General Teaching Model Based on Educational Neuroscience Studies

Background and Objective: This study aimed to review the general teaching model based on educational neuroscience studies. Methods and Materials: The research was conducted using a qualitative approach and deductive content analysis method. The study involved extensive research of digital resources, journals, books, and dissertations from 1990 to 2021 using databases such as ERIC, Springer, ScienceDirect, Wiley, Google Scholar, Library, Genesis Libdl, Knowledge E, and SID. Keywords in both Persian and English, including teaching method, brain-based learning, and educational neuroscience, were used in the search. A total of 183 relevant articles and books were identified, from which those addressing brain-compatible learning were selected. Findings: After collecting information from texts and analyzing the content, removing duplicate codes, and merging similar codes, 1416 codes were obtained. These were organized into 109 initial categories, 31 sub-categories, and 4 main categories within the general teaching model, covering pre-teaching, pre-and during teaching, during teaching, and post-teaching stages. The study's results explained 28 educational actions with an educational neuroscience approach in the stages of the general teaching model. These were validated through confirmatory factor analysis by experts and specialists in educational neuroscience (psychology, cognitive sciences, and educational sciences) and confirmed. Conclusion: It appears that implementing actions derived from educational neuroscience studies in the stages of the general teaching model can improve learners' brain performance and consequently enhance their learning.

A Generative AI-Driven Method-level Semantic Clone Detection based on the Structural and Semantical Comparison of Methods

A Generative AI-Driven Method-level Semantic Clone Detection based on the Structural and Semantical Comparison of Methods

Transcompiler CNN-GMN Based Cross Language Clone Detection and Algorithm Recognition

Abstract: Software replication stemming from code reutilization introduces complexities in software maintenance. While deep learning-driven clone detection tools abound, they predominantly cater to singular programming languages. Concurrently, the task of identifying algorithms within program code remains challenging due to the absence of metadata, rendering the selection process intricate. This research seeks to establish synergies between these disparate domains by advancing innovative methodologies. The study delves into the unexplored domain of harnessing data augmentation, particularly leveraging transcompiler-based techniques, to enhance the discernment of code clones across different programming languages. Drawing upon the insights derived from transcompilers, this investigation applies data augmentation through source-to-source translation, exemplified by the Transcoder. This method extends the applicability of single-language models, such as the Graph Matching Network (GMN), to encompass cross-language detection. Concurrently, a program code classification model is introduced, leveraging Convolutional Neural Networks (CNNs) to discern algorithms based on structural features (SFs). These SFs are extracted from program codes and subsequently transformed into a one-hot binary matrix. The CNN model undergoes meticulous fine-tuning, optimizing its structural configurations and hyperparameters for superior algorithm classification. This research signifies an integration of disparate realms, intertwining cross-language clone detection and algorithm identification within a unified framework. The exploration of data augmentation for cross-language clone detection and the employment of CNN-GMNs for algorithm identification converge to furnish significant contributions to both domains. These insights, thus, hold promise for advancing the fields of software engineering and programming education

On the impact of multiple source code representations on software engineering tasks — An empirical study

Efficiently representing source code is crucial for various software engineering tasks such as code classification and clone detection. Existing approaches primarily use Abstract Syntax Tree (AST), and only a few focus on semantic graphs such as Control Flow Graph (CFG) and Program Dependency Graph (PDG), which contain information about source code that AST does not. Even though some works tried to utilize multiple representations, they do not provide any insights about the costs and benefits of using multiple representations. The primary goal of this paper is to discuss the implications of utilizing multiple source code representations, specifically AST, CFG, and PDG. We modify an AST path-based approach to accept multiple representations as input to an attention-based model. We do this to measure the impact of additional representations (such as CFG and PDG) over AST. We evaluate our approach on three tasks: Method Naming, Program Classification, and Clone Detection. Our approach increases the performance on these tasks by 11% (F1), 15.7% (Accuracy), and 9.3% (F1), respectively, over the baseline. In addition to the effect on performance, we discuss timing overheads incurred with multiple representations. We envision that this work can provide a base for researchers to explore and experiment with a variety of source code representations for software engineering tasks.

Clinical factors for central nervous system progression and survival in primary vitreoretinal lymphoma.

Primary vitreoretinal lymphoma (PVRL) is a rare subtype of malignant lymphoma with a poor prognosis because of high frequency of central nervous system (CNS) progression. Identification of factors associated with CNS progression is essential to improve the prognosis of patients with PVRL. We conducted a retrospective study of 54 patients diagnosed with PVRL and treated at our hospital to identify factors associated with CNS progression and prognosis. All patients were treated with intravitreal methotrexate (MTX) injections in the affected eyes until lesion resolution. Twenty-four patients were treated with systemic administration of high-dose MTX (systemic HD-MTX) every other week for a total of five cycles following intravitreal MTX injection. Of 24 patients, 20 completed five cycles of systemic HD-MTX. The 5-year cumulative incidence of CNS progression and overall survival (OS) rate were 78.0% and 69.0% respectively. By univariate and multivariate analyses, bilateral disease and the detection of B-cell clonality confirmed by flow cytometric analysis were risk factors associated with CNS progression. Moreover, systemic HD-MTX completion reduced the risk of CNS progression and was identified as a factor affecting OS. In this study, factors for CNS progression identified may potentially contribute to the optimized therapeutic stratification to improve the survival of patients with PVRL.