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Detection of Antipatterns from Service Oriented Architecture

Thesis Info

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External Link

Author

Sabir, Fatima

Program

PhD

Institute

COMSATS University Islamabad

City

Islamabad

Province

Islamabad.

Country

Pakistan

Thesis Completing Year

2019

Thesis Completion Status

Completed

Subject

Computer Science

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/10935/1/Fatima%20Sabir_SE_2019_Comsats_PRR.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727730001

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Web-services have become a governing technology for Service Oriented Architectures due to reusability of services and their dependence on other services. The evolution in service-based systems demand frequent changes to provide quality of service to customers. It is realized by different authors that evolution in service-based systems may degrade design and quality of service and may generate poor solutions known as antipatterns. The detection of antipatterns from web services is an important research realm and it is continuously getting attention of researchers. There are several techniques and tools presented for detection of antipatterns from object-oriented software applications but only few approaches are presented for detection of antipatterns from SOA. The state of the art antipattern detection approaches presented for detection of antipatterns from SOA are not flexible. We present a flexible approach supplemented with a tool support named as SWAD (Specification of Webservice Antipatterns Detection) to detect antipatterns from different SOAP based applications. Service-based systems, in particular, RESTful APIs, need to meet both service consumers’ and providers’ requirements. Like other software systems, RESTful APIs face continuous maintenance and evolution. Antipatterns may hinder the maintenance and evolution of RESTful APIs, as compared to the good design principles, i.e., design patterns that facilitate maintenance and evolution. Antipatterns may also affect the usability of RESTful APIs. Major market players like Facebook and YouTube are already using REST architecture and their APIs are frequently evolving to meet the end users’ requirements. Although, a number of antipatterns are de researchers performed their automatic detection but the evolution of RESTful APIs did not receive much attention. There is a need to track the evolution of antipatterns in the RESTful APIs that could assist service providers publishing well-designed and easy to consume RESTful APIs for their clients. We present the correction of eight REST antipatterns in RESTful APIs with a tool support called SOCAR (Service Oriented Correction of Antipatterns in REST) after analyzing their evolution history for two years. Our correction heuristics are validated by practitioners with an average precision of 100% and an average recall of 94%. Moreover, we propose a methodology for the correction of linguistic antipatterns with a tool support COLAR (Correction of Linguistic Antipatterns for RESTAPIs).
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دل میں ایسا درد اٹھا ہے

دل میں ایسا درد اُٹھا ہے
سارا منظر چیخ رہا ہے

اُس کی یاد ہے دل سے گزری
دل میں کیا کیا شور مچا ہے

میری بات نہ مانے گا وہ
مجھ کو اُس کا خوب پتا ہے

دل اندر غم کی شدّت سے
خون کا اِک دریا بہتا ہے

یاد کے بوٹے سوکھ نہ جائیں
دل دریا پانی دیتا ہے

دل کی باتیں وہ کیا جانے
اُس کا دل تو پتھّر کا ہے

دل پر درد کا پتھّر رکھ کر
مجھ کو اُس جیسا بننا ہے

درد سے دل یہ بیٹھ نہ جائے
اب مجھ کو یہ ڈر لگتا ہے

جھوٹ تو بول نہیں سکتا میں
صادقؔ کا مطلب سچّا ہے

احادیث کی روشنی میں عصر حاضر کے چند اہم مالی معاملات کا تحقیقی جائزہ

Islam is a global religion and has provided a comprehensive code of life applicable in every age. Economy is one of its foremost priorities and gives basic principles to its followers to exercise a legal and favorable mean of earning. In Islamic economic system there are many ways and sources that have been prohibited or have not been recommended. As in our daily life we face those modes of financing, so in this article they have been comparatively analyzed in light of the sayings of the Prophet (S.A.W) in order to explore its legitimacy.

Developing a Sindhi Computational Resource Grammar in Lexical Functional Grammar Framework

Computational grammar development and deep linguistic analysis provides structural details for natural language understanding by machines. Modern multilingual information processing systems use these details for understanding and processing of information represented in different languages. While work in Sindhi language is focused in the areas like part of speech tagging and machine learning. Sindhi lacks resources like computational grammars and deep linguistic analysis systems. Development of such resources is open research area in computational linguistic and natural language processing domains. This work presents the development of Sindhi language morphology and grammar in Finite State Technology and Lexical Functional Grammar (LFG) frameworks. The work includes the investigation and identification of morphology and syntax patterns in Sindhi language, development of Sindhi finite state lexicon by modeling of identified morphological patters in LEXC, development of Sindhi LFG by incorporating the finite state lexicon in XLE, and evaluation of developed morphological lexicon and LFG grammar. Various parts of speech of Sindhi language are investigated and their morphological patterns are identified. Nouns are marked by number, gender and case. Ten different cases of nouns are identified namely nominative, accusative, dative, participant, instrumental, locative, ablative, agentive, genitive and vocative. Adjectives are also declined like nouns. Pronouns are declined for number and gender and are marked by nominative, oblique and genitive cases. Generally, adverbs are not inflected but when adjectives used as adverbs they hold the inflectional properties of adjectives. Genitive iv postpositions are inflected and marked by number and gender. Conjunctions and interjections do not inflect. Verbs are most complex part of speech and classified into main, auxiliary, copula and modal verbs. Verbs are conjugated by number and gender and are marked by tense, aspect and mood. Morphological analysis of developed model shows that a verb can have up to 75 different morphological forms in Sindhi. Present, past and future tense patterns along with aspect and mood are analyzed. Aspect in Sindhi can either be perfective or imperfective (continuous and habitual) and can be marked morphologically or syntactically. Many alternative patterns of different aspects exist. Nine different mood patterns are identified including subjunctive, presumptive, imperative, declarative, permissive, prohibitive, capacitive, compulsive and suggestive. Pronominal suffixes in Sindhi may appear on nouns, postpositions and verbs. Pronominal suffixation can possibly cause subject and object pro-drop. Sindhi syntax is analyzed with LFG perspective. Different noun phrase constructions are implemented with coordination patterns including adjective phrases, postpositional phrases, participle phrases, and relative clauses. Genitive case marking patterns along with syntactic agreement are identified and modeled in LFG. Verbal subcategorization frames are defined for different grammatical functions including SUBJ (Subject), OBJ (Object), OBJ2 (Secondary Object), OBL (Oblique), COMP (Complement), XCOMP (Open Complement), and PREDLINK (Predicate link). Phrase and sentence level adjuncts (ADJUNCT) and open adjunct (XADJUNCT) patterns are also identified and implemented in LFG. The developed grammar is tested against two different test suites. First v test suite contains 617 handcrafted sentences in 10 different test files containing sentences with different syntactic features. Second test suite contains real time corpus of two text books of Sindhi class one with 258 sentences. Results show 98.05% and 96.5% parsing percentage of test suite 1 and test suite 2 respectively. Morphology coverage includes 862 stems of different POS classes with total of 10327 inflectional forms. The developed finite state morphology is tested and evaluated against the corpus of 9050 words in terms of coverage, ambiguity, precision, recall and f-measure (F1). The results show 97.8% precision, 96.08% recall and average ambiguity of 1.65 solutions per word with 91.1% coverage. Coverage of different morphological features include number, gender, case, tense, aspect and mood. Syntactic coverage includes nominal elements, coordination, subordination, agreement, verbal subcategorization, tense, aspect and mood. Research and development results include Sindhi part of speech tagset, roman script for Sindhi language, morphological lexicon and LFG grammar of Sindhi. As a side development, a corpus of about 4 million words is also developed. In absence of linguistic resources for Sindhi language, these developments will have signification impact on Sindhi language processing and further research in computational linguistics and related domains.