An Empirical Assessment of Bellon's Clone Benchmark

Context: Clone benchmarks are essential to the assessment and improvement of clone detection tools and algorithms. Among existing benchmarks, Bellon’s benchmark is widely used by the research community. However, a serious threat to the validity of this benchmark is that reference clones it contains have been manually validated by Bellon alone. Other persons may disagree with Bellon’s judgment. Ob-jective: In this paper, we perform an empirical assessment of Bellon’s benchmark. Method: We seek the opinion of eighteen participants on a subset of Bellon’s benchmark to determine if researchers should trust the reference clones it contains. Results: Our experiment shows that a significant amount of the reference clones are debatable, and this phe-nomenon can introduce noise in results obtained using this benchmark

Raters’ reliability in clone benchmarks construction

International audienceCloned code often complicates code maintenance and evolution and must therefore be effectively detected. One of the biggest challenges for clone detectors is to reduce the amount of irrelevant clones they found, called false positives. Several benchmarks of true and false positive clones have been introduced, enabling tool developers to compare, assess and fine-tune their tools. Manual inspection of clone candidates is performed by raters that do not have expertise on the underlying code. This way of building benchmarks might be unreliable when considering context-dependent clones i.e., clones valid for a specific purpose. Our goal is to investigate the reliability of rater judgments about context-dependent clones. We randomly select about 600 clones from two projects and ask several raters, including experts of the projects, to manually classify these clones. We observe that judgments of non expert raters are not always repeatable. We also observe that they seldomly agree with each others and with the expert. Finally, we find that the project and the fact that a clone is a true or false positive might have an influence on the agreement between the expert and non experts. Therefore, using non experts to produce clone benchmarks could be unreliable

A systematic literature review on the code smells datasets and validation mechanisms

The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant properties such as the size, severity level, project types, number of each type of smell, number of smells, and the ratio of smelly to non-smelly samples in the dataset. Most existing datasets support God Class, Long Method, and Feature Envy while six smells in Fowler and Beck's catalog are not supported by any datasets. We conclude that existing datasets suffer from imbalanced samples, lack of supporting severity level, and restriction to Java language.Comment: 34 pages, 10 figures, 12 tables, Accepte

Efficiently Measuring an Accurate and Generalized Clone Detection Precision using Clone Clustering

Abstract-An important measure of clone detection performance is precision. However, there has been a marked lack of research into methods of efficiently and accurately measuring the precision of a clone detection tool. Instead, tool authors simply validate a small random sample of the clones their tools detected in a subject software system. Since there could be many thousands of clones reported by the tool, such a small random sample cannot guarantee an accurate and generalized measure of the tool's precision for all the varieties of clones that can occur in any arbitrary software system. In this paper, we propose a machine-learning based approach that can cluster similar clones together, and which can be used to maximize the variety of clones examined when measuring precision, while significantly reducing the biases a specific subject system has on the generality of the precision measured. Our technique reduces the efforts in measuring precision, while doubling the variety of clones validated and reducing biases that harm the generality of the measure by up to an order of magnitude. Our case study with the NiCad clone detector and the Java class library shows that our approach is effective in efficiently measuring an accurate and generalized precision of a subject clone detection tool

Towards Collaborative Scientific Workflow Management System

The big data explosion phenomenon has impacted several domains, starting from research areas to divergent of business models in recent years. As this intensive amount of data opens up the possibilities of several interesting knowledge discoveries, over the past few years divergent of research domains have undergone the shift of trend towards analyzing those massive amount data. Scientific Workflow Management System (SWfMS) has gained much popularity in recent years in accelerating those data-intensive analyses, visualization, and discoveries of important information. Data-intensive tasks are often significantly time-consuming and complex in nature and hence SWfMSs are designed to efficiently support the specification, modification, execution, failure handling, and monitoring of the tasks in a scientific workflow. As far as the complexity, dimension, and volume of data are concerned, their effective analysis or management often become challenging for an individual and requires collaboration of multiple scientists instead. Hence, the notion of 'Collaborative SWfMS' was coined - which gained significant interest among researchers in recent years as none of the existing SWfMSs directly support real-time collaboration among scientists. In terms of collaborative SWfMSs, consistency management in the face of conflicting concurrent operations of the collaborators is a major challenge for its highly interconnected document structure among the computational modules - where any minor change in a part of the workflow can highly impact the other part of the collaborative workflow for the datalink relation among them. In addition to the consistency management, studies show several other challenges that need to be addressed towards a successful design of collaborative SWfMSs, such as sub-workflow composition and execution by different sub-groups, relationship between scientific workflows and collaboration models, sub-workflow monitoring, seamless integration and access control of the workflow components among collaborators and so on. In this thesis, we propose a locking scheme to facilitate consistency management in collaborative SWfMSs. The proposed method works by locking workflow components at a granular attribute level in addition to supporting locks on a targeted part of the collaborative workflow. We conducted several experiments to analyze the performance of the proposed method in comparison to related existing methods. Our studies show that the proposed method can reduce the average waiting time of a collaborator by up to 36% while increasing the average workflow update rate by up to 15% in comparison to existing descendent modular level locking techniques for collaborative SWfMSs. We also propose a role-based access control technique for the management of collaborative SWfMSs. We leverage the Collaborative Interactive Application Methodology (CIAM) for the investigation of role-based access control in the context of collaborative SWfMSs. We present our proposed method with a use-case of Plant Phenotyping and Genotyping research domain. Recent study shows that the collaborative SWfMSs often different sets of opportunities and challenges. From our investigations on existing research works towards collaborative SWfMSs and findings of our prior two studies, we propose an architecture of collaborative SWfMSs. We propose - SciWorCS - a Collaborative Scientific Workflow Management System as a proof of concept of the proposed architecture; which is the first of its kind to the best of our knowledge. We present several real-world use-cases of scientific workflows using SciWorCS. Finally, we conduct several user studies using SciWorCS comprising different real-world scientific workflows (i.e., from myExperiment) to understand the user behavior and styles of work in the context of collaborative SWfMSs. In addition to evaluating SciWorCS, the user studies reveal several interesting facts which can significantly contribute in the research domain, as none of the existing methods considered such empirical studies, and rather relied only on computer generated simulated studies for evaluation

Management Aspects of Software Clone Detection and Analysis

Copying a code fragment and reusing it by pasting with or without minor modifications is a common practice in software development for improved productivity. As a result, software systems often have similar segments of code, called software clones or code clones. Due to many reasons, unintentional clones may also appear in the source code without awareness of the developer. Studies report that significant fractions (5% to 50%) of the code in typical software systems are cloned. Although code cloning may increase initial productivity, it may cause fault propagation, inflate the code base and increase maintenance overhead. Thus, it is believed that code clones should be identified and carefully managed. This Ph.D. thesis contributes in clone management with techniques realized into tools and large-scale in-depth analyses of clones to inform clone management in devising effective techniques and strategies. To support proactive clone management, we have developed a clone detector as a plug-in to the Eclipse IDE. For clone detection, we used a hybrid approach that combines the strength of both parser-based and text-based techniques. To capture clones that are similar but not exact duplicates, we adopted a novel approach that applies a suffix-tree-based k-difference hybrid algorithm, borrowed from the area of computational biology. Instead of targeting all clones from the entire code base, our tool aids clone-aware development by allowing focused search for clones of any code fragment of the developer's interest. A good understanding on the code cloning phenomenon is a prerequisite to devise efficient clone management strategies. The second phase of the thesis includes large-scale empirical studies on the characteristics (e.g., proportion, types of similarity, change patterns) of code clones in evolving software systems. Applying statistical techniques, we also made fairly accurate forecast on the proportion of code clones in the future versions of software projects. The outcome of these studies expose useful insights into the characteristics of evolving clones and their management implications. Upon identification of the code clones, their management often necessitates careful refactoring, which is dealt with at the third phase of the thesis. Given a large number of clones, it is difficult to optimally decide what to refactor and what not, especially when there are dependencies among clones and the objective remains the minimization of refactoring efforts and risks while maximizing benefits. In this regard, we developed a novel clone refactoring scheduler that applies a constraint programming approach. We also introduced a novel effort model for the estimation of efforts needed to refactor clones in source code. We evaluated our clone detector, scheduler and effort model through comparative empirical studies and user studies. Finally, based on our experience and in-depth analysis of the present state of the art, we expose avenues for further research and development towards a versatile clone management system that we envision