PENINGKATAN AKURASI TOPIC MODELING PADA KINERJA KEPOLISIAN REPUBLIK INDONESIA DI TWITTER MENGGUNAKAN ALGORITMA SPELL CHECKER

Kinerja polisi republik Indonesia saat ini menjadi soratan terutama munculnya banyak komentar dari netizen terutama di Twitter dengan diramaikannya hashtag #percumalaporpolisi dan #1hari1oknum. Hal ini mesti direspon cepat oleh kepolisian republik indonesia untuk melakukan counter issue dengan pertama-tama menggali topik tersembunyi dari sekian banyak tweet yang beredar salah satu caranya yaitu menerapkan topic modeling pada Twitter. Dalam rangka memetakan issue atau topik yang akan dibenahi menurut padangan publik terlebih dahulu. Salah satu kelemahan penerapan topic modeling ialah adanya typo maupun singkatan disengaja yang dapat menggangu keakuratan model yang akan dibangun. Maka penting diterapkannya peningkatan perbaikan teks tweet yang diakuisisi sebelum dimasukkan ke dalam model. Normalisasi teks dapat dikaloborasikan dengan algoritma spell checker yang dapat membantu menormalkan typo dan singkatan yang disengaja untuk membantu meningkatkan akurasi model. Penerapan algoritma spell cheker terbukti mampu meningkatkan akurasi model yang mengakibatkan topik yang dihasilkan sebelum dan sesudah diterapkan spell checker berbeda, ditandai dengan tidak ditemukannya lagi singkatan atau typo pada term yang diolah oleh LDA

Predicting change propagation using domain-based coupling

Most enterprise systems operate in domains where business rules and requirements frequently change. Managing the cost and impact of these changes has been a known challenge, and the software maintenance community has been tackling it for more than two decades. The traditional approach to impact analysis is by tracing dependencies in the design documents and the source code. More recently the software maintenance history has been exploited for impact analysis. The problem is that these approaches are difficult to implement for hybrid systems that consist of heterogeneous components. In today’s computer era, it is common to find systems of systems where each system was developed in a different language. In such environments, it is a challenge to estimate the change propagation between components that are developed in different languages. There is often no direct code dependency between these components, and they are maintained in different development environments by different developers. In addition, it is the domain experts and consultants who raise the most of the enhancement requests; however, using the existing change impact analysis methods, they cannot evaluate the impact and cost of the proposed changes without the support of the developers. This thesis seeks to address these problems by proposing a new approach to change impact analysis based on software domain-level information. This approach is based on the assumption that domain-level relationships are reflected in the software source code, and one can predict software dependencies and change propagation by exploiting software domain-level information. The proposed approach is independent of the software implementation, inexpensive to implement, and usable by domain experts with no requirement to access and analyse the source code. This thesis introduces domain-based coupling as a novel measure of the semantic similarity between software user interface components. The hypothesis is that the domain-based coupling between software components is correlated with the likelihood of the existence of dependencies and change propagation between these components. This hypothesis has been evaluated with two case studies: • A study of one of the largest open source enterprise systems demonstrates that architectural dependencies can be identified with an accuracy of more than 70% solely based on the domain-based coupling. • A study of 12 years’ maintenance history of the five subsystems of a significant sized proprietary enterprise system demonstrates that the co-change coupling derived from over 75,000 change records can be predicted solely using domain-based coupling, with average recall and precision of more than 60%, which is of comparable quality to other state-of-the-art change impact analysis methods. The results of these studies support our hypothesis that software dependencies and change propagation can be predicted solely from software domain-level information. Although the accuracy of such predictions are not sufficiently strong to completely replace the traditional dependency analysis methods; nevertheless, the presented results suggest that the domain-based coupling might be used as a complementary method or where analysis of dependencies in the code and documents is not a viable option

Configuring and Assembling Information Retrieval based Solutions for Software Engineering Tasks.

Information Retrieval (IR) approaches are used to leverage textual or unstructured data generated during the software development process to support various software engineering (SE) tasks (e.g., concept location, traceability link recovery, change impact analysis, etc.). Two of the most important steps for applying IR techniques to support SE tasks are preprocessing the corpus and configuring the IR technique, and these steps can significantly influence the outcome and the amount of effort developers have to spend for these maintenance tasks. We present the use of Genetic Algorithms (GAs) to automatically configure and assemble an IR process to support SE tasks. The approach named IR-GA determines the (near) optimal solution to be used for each step of the IR process without requiring any training. We applied IR-GA on three different SE tasks and the results of the study indicate that IR-GA outperforms approaches previously used in the literature, and that it does not significantly differ from an ideal upper bound that could be achieved by a supervised approach and a combinatorial approach