An Extended Stable Marriage Problem Algorithm for Clone Detection

Code cloning negatively affects industrial software and threatens intellectual property. This paper presents a novel approach to detecting cloned software by using a bijective matching technique. The proposed approach focuses on increasing the range of similarity measures and thus enhancing the precision of the detection. This is achieved by extending a well-known stable-marriage problem (SMP) and demonstrating how matches between code fragments of different files can be expressed. A prototype of the proposed approach is provided using a proper scenario, which shows a noticeable improvement in several features of clone detection such as scalability and accuracy.Comment: 20 pages, 10 figures, 6 table

Proof Repair Infrastructure for Supervised Models: Building a Large Proof Repair Dataset

We report on our efforts building a new, large proof-repair dataset and benchmark suite for the Coq proof assistant. The dataset is made up of Git commits from open-source projects with old and new versions of definitions and proofs aligned across commits. Building this dataset has been a significant undertaking, highlighting a number of challenges and gaps in existing infrastructure. We discuss these challenges and gaps, and we provide recommendations for how the proof assistant community can address them. Our hope is to make it easier to build datasets and benchmark suites so that machine-learning tools for proofs will move to target the tasks that matter most and do so equitably across proof assistants

A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table

Primer selection impacts specific population abundances but not community dynamics in a monthly time-series 16S rRNA gene amplicon analysis of coastal marine bacterioplankton.

Primers targeting the 16S small subunit ribosomal RNA marker gene, used to characterize bacterial and archaeal communities, have recently been re-evaluated for marine planktonic habitats. To investigate whether primer selection affects the ecological interpretation of bacterioplankton populations and community dynamics, amplicon sequencing with four primer sets targeting several hypervariable regions of the 16S rRNA gene was conducted on both mock communities constructed from cloned 16S rRNA genes and a time-series of DNA samples from the temperate coastal Santa Barbara Channel. Ecological interpretations of community structure (delineation of depth and seasonality, correlations with environmental factors) were similar across primer sets, while population dynamics varied. We observed substantial differences in relative abundances of taxa known to be poorly resolved by some primer sets, such as Thaumarchaeota and SAR11, and unexpected taxa including Roseobacter clades. Though the magnitude of relative abundances of common OTUs differed between primer sets, the relative abundances of the OTUs were nonetheless strongly correlated. We do not endorse one primer set but rather enumerate strengths and weaknesses to facilitate selection appropriate to a system or experimental goal. While 16S rRNA gene primer bias suggests caution in assessing quantitative population dynamics, community dynamics appear robust across studies using different primers

Loupe: Driving the Development of OS Compatibility Layers

Supporting mainstream applications is fundamental for a new OS to have impact. It is generally achieved by developing a layer of compatibility allowing applications developed for a mainstream OS like Linux to run unmodified on the new OS. Building such a layer, as we show, results in large engineering inefficiencies due to the lack of efficient methods to precisely measure the OS features required by a set of applications. We propose Loupe, a novel method based on dynamic analysis that determines the OS features that need to be implemented in a prototype OS to bring support for a target set of applications and workloads. Loupe guides and boosts OS developers as they build compatibility layers, prioritizing which features to implement in order to quickly support many applications as early as possible. We apply our methodology to 100+ applications and several OSes currently under development, demonstrating high engineering effort savings vs. existing approaches: for example, for the 62 applications supported by the OSv kernel, we show that using Loupe, would have required implementing only 37 system calls vs. 92 for the non-systematic process followed by OSv developers. We study our measurements and extract novel key insights. Overall, we show that the burden of building compatibility layers is significantly less than what previous works suggest: in some cases, only as few as 20% of system calls reported by static analysis, and 50% of those reported by naive dynamic analysis need an implementation for an application to successfully run standard benchmarks.Comment: Accepted to appear at ASPLOS'24 (https://www.asplos-conference.org/asplos2024/

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

On benchmarking of deep learning systems: software engineering issues and reproducibility challenges

Since AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, Deep Learning (and Machine Learning/AI in general) gained an exponential interest. Nowadays, their adoption spreads over numerous sectors, like automotive, robotics, healthcare and finance. The ML advancement goes in pair with the quality improvement delivered by those solutions. However, those ameliorations are not for free: ML algorithms always require an increasing computational power, which pushes computer engineers to develop new devices capable of coping with this demand for performance. To foster the evolution of DSAs, and thus ML research, it is key to make it easy to experiment and compare them. This may be challenging since, even if the software built around these devices simplifies their usage, obtaining the best performance is not always straightforward. The situation gets even worse when the experiments are not conducted in a reproducible way. Even though the importance of reproducibility for the research is evident, it does not directly translate into reproducible experiments. In fact, as already shown by previous studies regarding other research fields, also ML is facing a reproducibility crisis. Our work addresses the topic of reproducibility of ML applications. Reproducibility in this context has two aspects: results reproducibility and performance reproducibility. While the reproducibility of the results is mandatory, performance reproducibility cannot be neglected because high-performance device usage causes cost. To understand how the ML situation is regarding reproducibility of performance, we reproduce results published for the MLPerf suite, which seems to be the most used machine learning benchmark. Because of the wide range of devices and frameworks used in different benchmark submissions, we focus on a subset of accuracy and performance results submitted to the MLPerf Inference benchmark, presenting a detailed analysis of the difficulties a scientist may find when trying to reproduce such a benchmark and a possible solution using our workflow tool for experiment reproducibility: PROVA!. We designed PROVA! to support the reproducibility in traditional HPC experiments, but we will show how we extended it to be used as a 'driver' for MLPerf benchmark applications. The PROVA! driver mode allows us to experiment with different versions of the MLPerf Inference benchmark switching among different hardware and software combinations and compare them in a reproducible way. In the last part, we will present the results of our reproducibility study, demonstrating the importance of having a support tool to reproduce and extend original experiments getting deeper knowledge about performance behaviours

PeerJ

BackgroundAs next generation sequence technology has advanced, there have been parallel advances in genome-scale analysis programs for determining evolutionary relationships as proxies for epidemiological relationship in public health. Most new programs skip traditional steps of ortholog determination and multi-gene alignment, instead identifying variants across a set of genomes, then summarizing results in a matrix of single-nucleotide polymorphisms or alleles for standard phylogenetic analysis. However, public health authorities need to document the performance of these methods with appropriate and comprehensive datasets so they can be validated for specific purposes, e.g., outbreak surveillance. Here we propose a set of benchmark datasets to be used for comparison and validation of phylogenomic pipelines.MethodsWe identified four well-documented foodborne pathogen events in which the epidemiology was concordant with routine phylogenomic analyses (reference-based SNP and wgMLST approaches). These are ideal benchmark datasets, as the trees, WGS data, and epidemiological data for each are all in agreement. We have placed these sequence data, sample metadata, and \u201cknown\u201d phylogenetic trees in publicly-accessible databases and developed a standard descriptive spreadsheet format describing each dataset. To facilitate easy downloading of these benchmarks, we developed an automated script that uses the standard descriptive spreadsheet format.ResultsOur \u201coutbreak\u201d benchmark datasets represent the four major foodborne bacterial pathogens (Listeria monocytogenes, Salmonella enterica, Escherichia coli, and Campylobacter jejuni) and one simulated dataset where the \u201cknown tree\u201d can be accurately called the \u201ctrue tree\u201d. The downloading script and associated table files are available on GitHub: https://github.com/WGS-standards-and-analysis/datasets.DiscussionThese five benchmark datasets will help standardize comparison of current and future phylogenomic pipelines, and facilitate important cross-institutional collaborations. Our work is part of a global effort to provide collaborative infrastructure for sequence data and analytic tools\u2014we welcome additional benchmark datasets in our recommended format, and, if relevant, we will add these on our GitHub site. Together, these datasets, dataset format, and the underlying GitHub infrastructure present a recommended path for worldwide standardization of phylogenomic pipelines.29372115PMC578280