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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.
Test Case Grouping and Filtering for Better Performance of Spectrum-based Fault Localization
Spectrum-based fault localization (SFL) method assigns a suspicious ratio. The statement is strongly affected by a failed test case compared to a passed test case. A failed test case assigns a suspicious ratio while a passed test case reduces some parts of assigned suspicious ratio. In the absence of a failed test case, it is impossible to localize the fault. Thus, a failed test case is very important for fault localization. However, spectrum-based fault localization has difficulty in reflecting the unique characteristics of a failed test because a failed test case and a passed test case are input at the same time to calculate a suspicious ratio. This paper supplements for this limitation and suggests a test case grouping method for more accurate fault localization. In addition, this paper suggested a filtering method considering test efficiency and verified the effectiveness by applying 65 algorithms. In 90 % of whole methods, the accuracy was improved by 13% and the effectiveness was improved by 72% based on EXAM score.
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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