The main difference between Software Product Line Development (SPLD) and traditional Software Product Development (SPD) is that development artifacts of SPLD contain variation points to deal with variabilities of a product family. By applying the SPLD approach, we can develop a product family more efficiently than by applying the SPD approach to each product of the product family. It is because the SPLD approach allows us to reuse the common parts obtained from analyzing the commonalities and variabilities of a product family for product development. Therefore, both (1) variability modeling for a product family and (2) inconsistency analysis of the resulting variability model that ensures it has been modeled correctly are crucial activities for the success of SPLD.
Existing SPLD studies proposed various methods for analysis of various aspects of correctness of a variability model. In particular, analyzing whether a variability model is inconsistent or not is considered the most important analysis perspective since it is impossible to configure products from such a model. However, existing SPLD methods that analyze whether there is an inconsistency in a variability model cannot identify the exact locations of the variability model that cause the inconsistency.
To overcome these limitations of the existing SPLD studies, this thesis proposes an Inconsistency Cause Locating (ICL) method. This method analyzes whether there is inconsistency in a variability model and, if there is inconsistency, it locates all the causes of the inconsistency in the model. In particular, the proposed method locates the exact locations of the nodes and links of the variability model that cause the inconsistency. As a consequence, developers using the method can easily find and fix inconsistency regardless of the size of a variability model. The key idea behind the ICL method is that, when transforming the given variability model to a CNF formula, by assigning to each literal of the formula a unique index that corresponds to each node of the variability model, the nodes that cause inconsistency, if the model is inconsistent, can be traced back once conflicting literal clauses are detected in the CNF formula.
In this thesis, the ICL method is presented in the context of the variability models created by the Compositional Variability Modeling (CVM) method. This is because CVM method is a variability modeling method that can be covered throughout the entire SPLD process by solving and improving various problems of the traditional feature models. Specifically, the CVM method can systematically handle variability of a product family that occurs throughout the entire SPLD process by introducing notions of variation point and variant and clearly distinguishing between features and variability in a variability model. In addition, the CVM method can be used throughout the entire SPLD process because it is possible to construct a traceability relationship between a variability model and a development artifact and to provide compositional modeling mechanism to handle complexity and scalability of variability models. ICL method can also be applied to various variability models, including traditional feature models.
To evaluate the ICL method, the research develops an ICL tool that automatically performs all the steps of the method and uses it to conduct experiments with 49 models, including real-world variability models. As a result, the ICL tool accurately identifies all models with an inconsistency and locates all causes of inconsistency in them.
소프트웨어 제품라인 개발(Software Product Line Development, SPLD)에서 제품군의 공통성과 가변성을 분석하기 위한 가변성 모델링은 성공적인 제품라인 개발을 위한 필수적인 활동이다. 이 활동에서 만들어진 가변성 모델은 소프트웨어 제품라인 개발 전 공정에서 활용되기 때문에 가변성 모델을 올바르게 만드는 것은 매우 중요하다. 만약 가변성 모델에 모순이 있다면, 그 모델로부터 잠재적인 제품을 구성할 수 없기 때문에 가변성 모델의 분석을 통해 모순이 없는 가변성 모델을 만들어야 한다. 하지만, 가변성 모델 분석의 기존 SPLD 연구들은 가변성 모델에 모순이 있는지 여부는 분석할 수 있지만, 모순이 발생하는 원인을 찾지 못한다는 한계점이 있다. 따라서, 본 논문에서는 Inconsistency Cause Locating(ICL) 방법을 제안한다. ICL 방법은 가변성 모델에 모순이 있는지 여부를 분석하고, 만약 모순이 있다면 모순의 모든 원인들을 찾는다. 제안 방법을 평가하기 위하여, 이 방법의 모든 단계를 자동화한 ICL 도구를 개발하였다. 우리는 이 도구를 사용하여 실제 가변성 모델을 포함한 총 49개 모델을 대상으로 실험한 결과 각 가변성 모델에 모순이 있는지 여부를 정확하게 분석하였으며, 모순이 있는 모델들은 모순의 모든 원인들까지 정확하게 찾았다. ICL 방법은 가변성 모델에 모순이 발생하는 모든 원인들을 정확하고 효율적으로 찾는 것이 가능하기 때문에, 기존 SPLD연구들의 한계점을 상당히 극복하였다.
