Architecture Recovery of Legacy Software Systems Using Unsupervised Machine Learning Techniques

Perhaps the most \ud important aspect in maintaining software legacy systems is un-derstanding \ud their architecture. Architectural documentation is often \ud unavailable. Thus eforts need to be made to recover the \ud architectural design from the source code. This thesis addresses the \ud problem of recovering the architecture of software systems for \ud greater understanding, and modularizing them for greater \ud maintainability, using machine learning techniques.\ud We use clustering to obtain a high-level view of a software's \ud architecture, by identify-ing major sub-systems within it. For this \ud purpose, we analyze the behaviour of existing similarity and \ud distance measures when applied to software artifacts and keeping in \ud view software characteristics, yielding explanations to some \ud previously unanswered questions.We develop two new hierarchical \ud clustering algorithms that address the problem of ar-bitrary \ud decisions taken by existing hierarchical algorithms. We also propose \ud a similarity measure suitable for software clustering. The \ud performance of the proposed algorithms and similarity measure is \ud evaluated using internal and external assessment. Instead of using \ud only one expert decomposition for external assessment, as is \ud commonly done, we use decompositions prepared by 4-5 experts for \ud each test system. Such an approach allows us to validate the idea of \ud multiple views of a software system. Experiments carried out on ¯ve \ud open source legacy software systems show that the performance of our \ud proposed algorithm is better than previously used algorithms.\ud Interpreting the results of clustering algorithms is often \ud difficult. To make clusters easier to understand, we propose a \ud labeling scheme for clusters and compare two alter-native ranking \ud schemes that can be utilized for this purpose. We demonstrate how \ud the labels assigned by our scheme aid understanding of the \ud clustering process of clustering algorithms. We also provide a \ud comparison between cluster analysis and concept analysis as \ud modularization techniques, and give examples of their application to \ud different software structures, thus indicating the strengths and \ud limitations of the two techniques.\ud Finally, we use association rule mining to gain insight into the \ud low-level structure of software systems by examining relationships \ud between architectural quarks i.e. functions,global variables and \ud user de¯ned types. Metarule-guided association rule mining is used \ud to\ud identify problems within structured legacy systems. Re-engineering \ud patterns that present solutions to these problems are proposed. \ud Results for the test systems reveal interesting characteristics \ud which allow us to understand legacy systems and their evolution