Identifikasi Peluang Refaktorisasi Pada Kasus God Class Berdasarkan Kesamaan Konsep

Sebuah kelas dalam Pemrograman Berbasis Objek (PBO) harus memiliki desain yang spesifik untuk menangani dan mengimplementasikan sebuah konsep. Kelas harus memiliki tanggungjawab spesifik terhadap sebuah konsep yang diimplementasikan dalam operasi. Pada pengembangannya, seringkali pengembang perangkat lunak tidak memperhatikan desain konsep kelas sehingga kelas menjadi berkembang dan memiliki implementasi yang kompleks atau biasa disebut dengan “God Class”. Dalam hal ini diperlukan metode untuk mengidentifikasi peluang refaktorisasi God Class menjadi beberapa kelas baru yang nantinya akan direfaktorisasi menggunakan metode refaktorisasi kelas seperti ExtractClass. Pada penelitian sebelumnya, telah diusulkan metode identifikasi peluang refaktorisasi kelas menggunakan tiga pembobotan yaitu SSM (Structural Similariy between Methods), CDM (Call based Dependency between Methods) dan CSM (Conceptual Similarity between Methods). Perhitungan CSM telah dilakukan dengan pendekatan LSI (Latent Semantic Indexing). Namun metode penilaian kesamaan konsep tersebut tidak dapat mendeteksi kesamaan konsep pada kalimat penyusun nama operasi. Kecenderungan pengembang menggunakan pilihan kata yang memiliki makna dan penggunaan kalimat kerja dalam membentuk nama operasi menjadikan peluang pada penelitian ini untuk dikembangkannya perhitungan CSM menggunakan pendekatan semantik dan word dependency sebagai lokasi konsep pada nama operasi. Penelitian ini mengajukan metode dalam mengidentifikasi peluang refaktorisasi kelas dengan menghitung tingkat kedekatan konsep pada operasi. Operasi pada kelas diekstrak menjadi corpus dan dihitung nilai kedekatannya dengan operasi yang lain menggunakan SSM, CDM, dan CSM. Pada perhitungan CSM dilakukan penilaian kesamaan konsep secara semantik menggunakan kedekatan konsep term-term penyusun korpus menggunakan library Standford Dependency. Dimana untuk menghitung kedekatan konsep antar operasi diusulkan menggunakan pendekatan vector space models dengan memodifikasi pembobotan TF-IDF dengan word distance dan word dependency. Kemudian nilai kedekatan antar operasi tersebut direpresentasikan dalam bentuk graf dan dilakukan pemotongan graf menggunakan metode Max-Flow Min-Cut untuk mendapatkan identifikasi operasi yang harus dipisahkan dari kelas God Class. Dengan mekanisme tersebut diatas maka pemrogram mendapatkan rekomendasi peluang refaktorisasi kelas dengan mudah. Hasil penelitian ini mencapai hasil dengan tingkat precision 0,708% dan recall 0,936%. =========================================================== A class in Object-Oriented Programming (OOP) must have a specific de-sign for handle and implement a concept. The class must have a specific respon-sibility to a concept which is implemented in the methods. In its development, software developers often do not pay attention to the design concept of the class so that the class be growing and has a complex implementation or commonly called the "God Class". In this case the necessary methods to identify refactoring opportunities to split God Class into several new classes that will be refactored using methods such ExtractClass Class Refactoring. In previous studies, it has been proposed methods for identification of refactoring opportunities using the three weighting which is SSM (Structural Similariy between Methods), CDM (Call-based Dependency between Methods) and CSM (Conceptual Similarity be-tween Methods). CSM calculations have been done with the approach of LSI (La-tent Semantic Indexing). But the concept of similarity assessment methods can not detect a similar concept to the sentences making up the name of the opera-tion. The tendency of developers using choice words have meaning and use of the phrase work in shaping the operation name on this research creates the opportuni-ty for the development of CSM calculation using semantic approach and word dependency as a concept on the location name of the operation. This study propose a method for identifying refactoring opportunities by identifying the concept location of methods. Methods of classes will be extracted into a corpus and calculated the value of its similarity to other operations using the SSM, CDM, and CSM. In the calculation of CSM assessment semantically similar concept of using concept similarity detection using StandfordDependen-cy. Where to calculate the proposed operation to the similarities between the ap-proach vector space models by modifying the TF-IDF weighting distance and word by word dependency. Then the similarity between these operations repre-sented in the form of graphs and graph cuts made using Max-Flow Min-Cut to get identification operations must be separated from God class Class. With proposed method, software developer can easily get recomendation for class refactoring opportunities. The result of this research is precision 0,708% and recall 0,936%

Software module clustering: An in-depth literature analysis

Software module clustering is an unsupervised learning method used to cluster software entities (e.g., classes, modules, or files) with similar features. The obtained clusters may be used to study, analyze, and understand the software entities' structure and behavior. Implementing software module clustering with optimal results is challenging. Accordingly, researchers have addressed many aspects of software module clustering in the past decade. Thus, it is essential to present the research evidence that has been published in this area. In this study, 143 research papers from well-known literature databases that examined software module clustering were reviewed to extract useful data. The obtained data were then used to answer several research questions regarding state-of-the-art clustering approaches, applications of clustering in software engineering, clustering processes, clustering algorithms, and evaluation methods. Several research gaps and challenges in software module clustering are discussed in this paper to provide a useful reference for researchers in this field

Using Clustering Techniques to Guide Refactoring of Object-Oriented Classes

Much of the cost of software development is maintenance. Well structured software tends to be cheaper to maintain than poorly structured software, because it is easier to analyze and modify. The research described in this thesis concentrates on determining how to improve the structure of object-oriented classes, the fundamental unit of organization for object-oriented programs. Some refactoring tools can mechanically restructure object-oriented classes, given the appropriate inputs regarding what attributes and methods belong in the revised classes. We address the research question of determining what belongs in those classes, i.e., determining which methods and attributes most belong together and how those methods and attributes can be organized into classes. Clustering techniques can be useful for grouping entities that belong together; however, doing so requires matching an appropriate algorithm to the domain task and choosing appropriate inputs. This thesis identifies clustering techniques suitable for determining the redistribution of existing attributes and methods among object-oriented classes, and discusses the strengths and weaknesses of these techniques. It then describes experiments using these techniques as the basis for refactoring open source Java classes and the changes in the class quality metrics that resulted. Based on these results and on others reported in the literature, it recommends particular clustering techniques for particular refactoring problems. These clustering techniques have been incorporated into an open source refactoring tool that provides low-cost assistance to programmers maintaining object-oriented classes. Such maintenance can reduce the total cost of software development

Analyse von IT-Anwendungen mittels Zeitvariation

Performanzprobleme treten in der Praxis von IT-Anwendungen häufig auf, trotz steigender Hardwareleistung und verschiedenster Ansätze zur Entwicklung performanter Software im Softwarelebenszyklus. Modellbasierte Performanzanalysen ermöglichen auf Basis von Entwurfsartefakten eine Prävention von Performanzproblemen. Bei bestehenden oder teilweise implementierten IT-Anwendungen wird versucht, durch Hardwareskalierung oder Optimierung des Codes Performanzprobleme zu beheben. Beide Ansätze haben Nachteile: modellbasierte Ansätze werden durch die benötigte hohe Expertise nicht generell genutzt, die nachträgliche Optimierung ist ein unsystematischer und unkoordinierter Prozess. Diese Dissertation schlägt einen neuen Ansatz zur Performanzanalyse für eine nachfolgende Optimierung vor. Mittels eines Experiments werden Performanzwechselwirkungen in der IT-Anwendung identifiziert. Basis des Experiments, das Analyseinstrumentarium, ist eine zielgerichtete, zeitliche Variation von Start-, Endzeitpunkt oder Laufzeitdauer von Abläufen der IT-Anwendung. Diese Herangehensweise ist automatisierbar und kann strukturiert und ohne hohen Lernaufwand im Softwareentwicklungsprozess angewandt werden. Mittels der Turingmaschine wird bewiesen, dass durch die zeitliche Variation des Analyseinstrumentariums die Korrektheit von sequentiellen Berechnung beibehalten wird. Dies wird auf nebenläufige Systeme mittels der parallelen Registermaschine erweitert und diskutiert. Mit diesem praxisnahen Maschinenmodell wird dargelegt, dass die entdeckten Wirkzusammenhänge des Analyseinstrumentariums Optimierungskandidaten identifizieren. Eine spezielle Experimentierumgebung, in der die Abläufe eines Systems, bestehend aus Software und Hardware, programmierbar variiert werden können, wird mittels einer Virtualisierungslösung realisiert. Techniken zur Nutzung des Analyseinstrumentariums durch eine Instrumentierung werden angegeben. Eine Methode zur Ermittlung von Mindestanforderungen von IT-Anwendungen an die Hardware wird präsentiert und mittels der Experimentierumgebung anhand von zwei Szenarios und dem Android Betriebssystem exemplifiziert. Verschiedene Verfahren, um aus den Beobachtungen des Experiments die Optimierungskandidaten des Systems zu eruieren, werden vorgestellt, klassifiziert und evaluiert. Die Identifikation von Optimierungskandidaten und -potenzial wird an Illustrationsszenarios und mehreren großen IT-Anwendungen mittels dieser Methoden praktisch demonstriert. Als konsequente Erweiterung wird auf Basis des Analyseinstrumentariums eine Testmethode zum Validieren eines Systems gegenüber nicht deterministisch reproduzierbaren Fehlern, die auf Grund mangelnder Synchronisationsmechanismen (z.B. Races) oder zeitlicher Abläufe entstehen (z.B. Heisenbugs, alterungsbedingte Fehler), angegeben.Performance problems are very common in IT-Application, even though hardware performance is consistently increasing and there are several different software performance engineering methodologies during the software life cycle. The early model based performance predictions are offering a prevention of performance problems based on software engineering artifacts. Existing or partially implemented IT-Applications are optimized with hardware scaling or code tuning. There are disadvantages with both approaches: the model based performance predictions are not generally used due to the needed high expertise, the ex post optimization is an unsystematic and unstructured process. This thesis proposes a novel approach to a performance analysis for a subsequent optimization of the IT-Application. Via an experiment in the IT-Application performance interdependencies are identified. The core of the analysis is a specific variation of start-, end time or runtime of events or processes in the IT-Application. This approach is automatic and can easily be used in a structured way in the software development process. With a Turingmachine the correctness of this experimental approach was proved. With these temporal variations the correctness of a sequential calculation is held. This is extended and discussed on concurrent systems with a parallel Registermachine. With this very practical machine model the effect of the experiment and the subsequent identification of optimization potential and candidates are demonstrated. A special experimental environment to vary temporal processes and events of the hardware and the software of a system was developed with a virtual machine. Techniques for this experimental approach via instrumenting are stated. A method to determine minimum hardware requirements with this experimental approach is presented and exemplified with two scenarios based on the Android Framework. Different techniques to determine candidates and potential for an optimization are presented, classified and evaluated. The process to analyze and identify optimization candidates and potential is demonstrated on scenarios for illustration purposes and real IT-Applications. As a consistent extension a test methodology enabling a test of non-deterministic reproducible errors is given. Such non-deterministic reproducible errors are faults in the system caused by insufficient synchronization mechanisms (for example Races or Heisenbugs) or aging-related faults