Enhancement of generic code clone detection model for python application

Identical code fragments in different locations are recognized as code clones. There are four native terminologies of code clones concluded as Type-1, Type-2, Type-3 and Type-4. Code clones can be identified using various approaches and models. Generic Code Clone Detection (GCCD) model was created to detect all four terminologies of code clones through five processes. A prototype has been developed to detect code clones in Java programming language that starts with Pre-processing Transformation, Parameterization, Categorization and ends with the Match Detection process. Hence, this work targeted to enhance the prototype using a GCCD model to identify all clone types in Python language. Enhancements are done in the Pre-processing process and parameterization process of the GCCD model to fit the Python language criteria. Results are improved by finding the best constant value and suitable weightage according to Python language. Proposed enhancement results of the Python language clone detection in GCCD model imply that Public as the weightage indicator and def as the best constant value

Change Impact Analysis of Code Clones

Copying a code fragment and reusing it with or without modifications is known to be a frequent activity in software development. This results in exact or closely similar copies of code fragments, known as code clones, to exist in the software systems. Developers leverage the code reuse opportunity by code cloning for increased productivity. However, different studies on code clones report important concerns regarding the impacts of clones on software maintenance. One of the key concerns is to maintain consistent evolution of the clone fragments as inconsistent changes to clones may introduce bugs. Challenges to the consistent evolution of clones involve the identification of all related clone fragments for change propagation when a cloned fragment is changed. The task of identifying the ripple effects (i.e., all the related components to change) is known as Change Impact Analysis (CIA). In this thesis, we evaluate the impacts of clones on software systems from new perspectives and then we propose an evolutionary coupling based technique for change impact analysis of clones. First, we empirically evaluate the comparative stability of cloned and non-cloned code using fine-grained syntactic change types. Second, we assess the impacts of clones from the perspective of coupling at the domain level. Third, we carry out a comprehensive analysis of the comparative stability of cloned and non-cloned code within a uniform framework. We compare stability metrics with the results from the original experimental settings with respect to the clone detection tools and the subject systems. Fourth, we investigate the relationships between stability and bug-proneness of clones to assess whether and how stability contribute to the bug-proneness of different types of clones. Next, in the fifth study, we analyzed the impacts of co-change coupling on the bug-proneness of different types of clones. After a comprehensive evaluation of the impacts of clones on software systems, we propose an evolutionary coupling based CIA approach to support the consistent evolution of clones. In the sixth study, we propose a solution to minimize the effects of atypical commits (extra large commits) on the accuracy of the detection of evolutionary coupling. We propose a clustering-based technique to split atypical commits into pseudo-commits of related entities. This considerably reduces the number of incorrect couplings introduced by the atypical commits. Finally, in the seventh study, we propose an evolutionary coupling based change impact analysis approach for clones. In addition to handling the atypical commits, we use the history of fine-grained syntactic changes extracted from the software repositories to detect typed evolutionary coupling of clones. Conventional approaches consider only the frequency of co-change of the entities to detect evolutionary coupling. We consider both change frequencies and the fine-grained change types in the detection of evolutionary coupling. Findings from our studies give important insights regarding the impacts of clones and our proposed typed evolutionary coupling based CIA approach has the potential to support the consistent evolution of clones for better clone management

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

On the Stability of Software Clones: A Genealogy-Based Empirical Study

Clones are a matter of great concern to the software engineering community because of their dual but contradictory impact on software maintenance. While there is strong empirical evidence of the harmful impact of clones on maintenance, a number of studies have also identified positive sides of code cloning during maintenance. Recently, to help determine if clones are beneficial or not during software maintenance, software researchers have been conducting studies that measure source code stability (the likelihood that code will be modified) of cloned code compared to non-cloned code. If the presence of clones in program artifacts (files, classes, methods, variables) causes the artifacts to be more frequently changed (i.e., cloned code is more unstable than non-cloned code), clones are considered harmful. Unfortunately, existing stability studies have resulted in contradictory results and even now there is no concrete answer to the research question "Is cloned or non-cloned code more stable during software maintenance?" The possible reasons behind the contradictory results of the existing studies are that they were conducted on different sets of subject systems with different experimental setups involving different clone detection tools investigating different stability metrics. Also, there are four major types of clones (Type 1: exact; Type 2: syntactically similar; Type 3: with some added, deleted or modified lines; and, Type 4: semantically similar) and none of these studies compared the instability of different types of clones. Focusing on these issues we perform an empirical study implementing seven methodologies that calculate eight stability-related metrics on the same experimental setup to compare the instability of cloned and non-cloned code in the maintenance phase. We investigated the instability of three major types of clones (Type 1, Type 2, and Type 3) from different dimensions. We excluded Type 4 clones from our investigation, because the existing clone detection tools cannot detect Type 4 clones well. According to our in-depth investigation on hundreds of revisions of 16 subject systems covering four different programming languages (Java, C, C#, and Python) using two clone detection tools (NiCad and CCFinder) we found that clones generally exhibit higher instability in the maintenance phase compared to non-cloned code. Specifically, Type 1 and Type 3 clones are more unstable as well as more harmful compared to Type 2 clones. However, although clones are generally more unstable sometimes they exhibit higher stability than non-cloned code. We further investigated the effect of clones on another important aspect of stability: method co-changeability (the degree methods change together). Intuitively, higher method co-changeability is an indication of higher instability of software systems. We found that clones do not have any negative effect on method co-changeability; rather, cloning can be a possible way of minimizing method co-changeability when clones are likely to evolve independently. Thus, clones have both positive and negative effects on software stability. Our empirical studies demonstrate how we can effectively use the positive sides of clones by minimizing their negative impacts

Code similarity and clone search in large-scale source code data

Software development is tremendously benefited from the Internet by having online code corpora that enable instant sharing of source code and online developer's guides and documentation. Nowadays, duplicated code (i.e., code clones) not only exists within or across software projects but also between online code repositories and websites. We call them "online code clones."' They can lead to license violations, bug propagation, and re-use of outdated code similar to classic code clones between software systems. Unfortunately, they are difficult to locate and fix since the search space in online code corpora is large and no longer confined to a local repository. This thesis presents a combined study of code similarity and online code clones. We empirically show that many code snippets on Stack Overflow are cloned from open source projects. Several of them become outdated or violate their original license and are possibly harmful to reuse. To develop a solution for finding online code clones, we study various code similarity techniques to gain insights into their strengths and weaknesses. A framework, called OCD, for evaluating code similarity and clone search tools is introduced and used to compare 34 state-of-the-art techniques on pervasively modified code and boiler-plate code. We also found that clone detection techniques can be enhanced by compilation and decompilation. Using the knowledge from the comparison of code similarity analysers, we create and evaluate Siamese, a scalable token-based clone search technique via multiple code representations. Our evaluation shows that Siamese scales to large-scale source code data of 365 million lines of code and offers high search precision and recall. Its clone search precision is comparable to seven state-of-the-art clone detection tools on the OCD framework. Finally, we demonstrate the usefulness of Siamese by applying the tool to find online code clones, automatically analyse clone licenses, and recommend tests for reuse