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Architecture Recovery of Legacy Software Systems Using Unsupervised Machine Learning Techniques

Thesis Info

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External Link

Author

Maqbool, Onaiza

Program

PhD

Institute

Lahore University of Management Sciences

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2006

Thesis Completion Status

Completed

Subject

Computer Science

Language

English

Link

http://prr.hec.gov.pk/jspui/handle/123456789/462

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727705163

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

ڈاکٹر راج بہادر گوڑاس
افسوس گذشتہ دنوں اردو کے شیدائی ڈاکٹر راج بہادر گوڑ اس دنیا سے رخصت ہوگئے، سرزمین اردو حیدرآباد میں پیدا ہوئے، ساری عمر اپنے نظریات کی اشاعت کے لیے جدوجہد کرتے رہے لیکن اردو سے بچپن سے جو رشتہ قائم ہوا تادم آخر اس کی حفاظت کرتے رہے، دارالمصنفین سے محبت رکھتے تھے، کئی بار یہاں آئے اور خوش ہوکر گئے، ان کی شائستہ شخصیت مشرقی قدروں کا دلکش نمونہ تھی، افسوس ایسے لوگ کم ہوتے جاتے ہیں، افسوس اس کا بھی ہے کہ ہم نے اپنا غم اور اردو کے نقصان کا اظہار تاخیر سے کیا۔
( عمیر الصدیق دریابادی ندوی ، جنوری ۲۰۱۲ء )

 

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