제품군의 재사용 가능한 클론 코드의 메소드 경로 통일을 위한코드 클러스터링 방법
A Code Clustering Technique for Unifying Method Full Path of Reusable Cloned Code Sets of a Product Family
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초록
유사한 소프트웨어는 기존 산출물을 복제하고 수정하는 클론-앤-오운(clone-and-own, CAO) 방법으로 개발되곤 한다. 그러나 클론-앤-오운방법은 복제된 제품의 수가 늘면서 유지보수를 어렵게 만들기 때문에 나쁜 프랙티스로 간주된다. 소프트웨어 제품라인 공학은 체계적인 재사용을통해 소프트웨어 제품군을 개발하는 방법으로 클론-앤-오운 방법의 문제를 해결할 수 있다. CAO 방식으로 개발되어 온 제품패밀리를 제품라인공학으로 마이그레이션하는 작업은 여러 소프트웨어 제품에서 클로닝된 부분들을 찾아 통합하고 재사용 가능한 자산으로 구축하는 것으로부터시작된다. 그러나 클로닝이 디렉토리부터 코드 라인까지 다양한 수준에서 발생하고 그 과정에서 이들의 구조에 변경이 일어날 수 있어 단순하게클로닝을 찾아내는 것만으로는 고품질의 제품라인 코드베이스를 구축하기 어렵다. 성공적인 마이그레이션을 위해서는 소스 코드들 사이의 클로닝관계를 찾는 것 이외에도 소스 코드들의 파일 경로와 클래스 이름, 메소드 시그니처 등의 동일성을 확보는 작업이 선행되어야 한다. 이에 본 연구는CAO 기반으로 개발된 제품들로부터 마이그레이션 대상 제품들을 선정한 후 제품들에 흩어져 있는 유사 코드 집합을 검출하여 메소드 경로의통일이 필요한 대상을 식별하는 클러스터링 방법을 제안한다. 제안 방법의 효과를 보이기 위해 CAO 방식으로 진화해온 ApoGames 제품군에 제안방법을 적용하여 실험을 진행하였다. 그 결과, 전처리 없이 수행된 파일의 상대 경로 기반 클러스터링 방법의 평균 정밀도는 0.91이며 식별된공통 클러스터의 개수는 0개인 반면에 이 논문에서 제안하는 전처리와 함께 수행된 메소드 시그니처 기반 클러스터링 방법의 평균 정밀도는 0.98로개선되었으며 식별된 공통 클러스터는 최대 15개까지 증가하였다.
Abstract
Similar software is often developed with the Clone-And-Own (CAO) approach that copies and modifies existing artifacts. The CAOapproach is considered as a bad practice because it makes maintenance difficult as the number of cloned products increases. Softwareproduct line engineering is a methodology that can solve the issue of the CAO approach by developing a product family through systematicreuse. Migrating product families that have been developed with the CAO approach to the product line engineering begins with finding,integrating, and building them as reusable assets. However, cloning occurs at various levels from directories to code lines, and theirstructures can be changed. This makes it difficult to build product line code base simply by finding clones. Successful migration thusrequires unifying the source code's file path, class name, and method signature. This paper proposes a clustering method that identifiesa set of similar codes scattered across product variants and some of their method full paths are different, so path unification is necessary. In order to show the effectiveness of the proposed method, we conducted an experiment using the Apo Games product line, whichhas evolved with the CAO approach. As a result, the average precision of clustering performed without preprocessing was 0.91 and thenumber of identified common clusters was 0, whereas our method showed 0.98 and 15 respectively
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