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Information Retrieval-based Bug Localization for Korean Bug Reports using Translation
http://doi.org/10.5626/JOK.2024.51.9.827
Information retrieval-based bug localization technique uses bug reports as queries to automatically identify faulty source files, significantly reducing the time developers spend locating bugs. The core of this technique lies in calculating text similarity between bug reports and source files. However, for bug reports written in Korean, the text similarity might not be effective due to difficulty of matching words with source codes primarily written in English. This study proposed an information retrieval-based bug localization technique for Korean bug reports using translation, enabling Korean developers to effectively use this technique. We also applied a soft voting method to effectively leverage outputs of multiple translators. To validate the performance of the proposed technique, we collected 269 Korean bug reports and conducted experiments using three translators and two ranking models. Experimental results showed that the proposed method improved bug localization performance by 44% compared to baselines.
Bug Report Quality Prediction for Enhancing Performance of Information Retrieval-based Bug Localization
Misoo Kim, June Ahn, Eunseok Lee
http://doi.org/10.5626/JOK.2017.44.8.832
Bug reports are essential documents for developers to localize and fix bugs. These reports contain information regarding software bugs or failures that occur during software operation and maintenance phase. Information Retrieval-based Bug Localization (IR-BL) techniques have been proposed to reduce the time and cost it takes for developers to resolve bug reports. However, if a low-quality bug report is submitted, the performance of such techniques can be significantly degraded. To address this problem, we propose a quality prediction method that selects low-quality bug reports. This process; defines a Quality property of a Bug report as a Query (Q4BaQ) and predicts the quality of the bug reports using machine learning. We evaluated the proposed method with 3 open source projects. The results of the experiment show that the proposed method achieved an average F-measure of 87.31% and outperformed previous prediction techniques by up to 6.62% in the F-measure. Finally, a combination of the proposed method and traditional automatic query reformulation method improved the MRR and MAP by 0.9% and 1.3%, respectively.
Dynamic Decision Making for Self-Adaptive Systems Considering Environment Information
Misoo Kim, Hohyeon Jeong, Eunseok Lee
Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.
An Extended DDN based Self-Adaptive System
Misoo Kim, Hohyeon Jeong, Eunseok Lee
In order to solve problems happening in the practical environment of complicated system, the importance of the self-adaptive system has recently begun to emerge. However, since the differences between the model built at the time of system design and the practical environment can lead the system into unpredictable situations, the study into methods of dealing with it is also emerging as an important issue. In this paper, we propose a method for deciding on the adaptation time in an uncertain environment, and reflecting the real-time environment in the system’s model. The proposed method calculates the Bayesian Surprise for the suitable adaptation time by comparing previous and current states, and then reflects the result following the performed policy in the design model to help in deciding the proper policy for the actual environment. The suggested method is applied to a navigation system to confirm its effectiveness.
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