This page has only limited features, please log in for full access.

Mr. Jonggu Kang
School of Computing, KAIST

Basic Info


Research Keywords & Expertise

0 Digital Twin
0 Industrial Software Defect Prediction
0 Software In Practice
0 AI/IoT
0 Future Mobility

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 24 February 2021 in Applied Sciences
Reads 0
Downloads 0

Software is playing the most important role in recent vehicle innovations, and consequently the amount of software has rapidly grown in recent decades. The safety-critical nature of ships, one sort of vehicle, makes software quality assurance (SQA) a fundamental prerequisite. Just-in-time software defect prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in the maritime domain reported feasible prediction performance. However, we still consider that the prediction model has room for improvement since the parameters of the model are not optimized yet. Harmony search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond the baseline of a previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite the fact that we considered balance as a performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with a high class imbalance ratio. Finally, we expect that our research can be extended to improve the performance of JIT-SDP not only in maritime domain software but also in open source software.

ACS Style

Jonggu Kang; Sunjae Kwon; Duksan Ryu; Jongmoon Baik. HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software. Applied Sciences 2021, 11, 2002 .

AMA Style

Jonggu Kang, Sunjae Kwon, Duksan Ryu, Jongmoon Baik. HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software. Applied Sciences. 2021; 11 (5):2002.

Chicago/Turabian Style

Jonggu Kang; Sunjae Kwon; Duksan Ryu; Jongmoon Baik. 2021. "HASPO: Harmony Search-Based Parameter Optimization for Just-in-Time Software Defect Prediction in Maritime Software." Applied Sciences 11, no. 5: 2002.

Special issue paper
Published: 04 November 2020 in Software: Practice and Experience
Reads 0
Downloads 0

Background The importance of software in maritime transportation is rapidly increasing as the industry seeks to develop and utilize innovative future ships, which can be realized using software technology. Due to the safety‐critical nature of ships, software quality assurance (SQA) has become an essential prerequisite for such development. Objective Based on the unique characteristics of the maritime domain, the purpose of this study was to achieve effective SQA resource allocation to reduce post‐release quality costs. Method Software defect prediction (SDP) is employed to predict defects in newly developed software based on models trained with past software defects and to update information using machine learning. This study demonstrated that just‐in‐time SDP is applicable to maritime domain practice and can reduce post‐release quality costs via combination with an estimation model, qCOPLIMO. Results Using real‐world datasets collected from the maritime industry, performance and cost‐benefit analyses of SDP were performed. A successful model was obtained that meets the performance criterion of 0.75 in within‐project defect prediction (WPDP) but not cross‐project defect prediction (CPDP). In addition, the cost‐benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post‐release quality cost can be reduced by 37.3% in the maritime domain. Conclusion SDP can be successfully applied to the maritime domain. Further, it is desirable to utilize WPDP instead of CPDP once minimum high‐quality commits are available that can be identified as defective or not. Finally, SDP can help reduce review effort and post‐release quality costs.

ACS Style

Jonggu Kang; Duksan Ryu; Jongmoon Baik. Predicting just‐in‐time software defects to reduce post‐release quality costs in the maritime industry. Software: Practice and Experience 2020, 51, 748 -771.

AMA Style

Jonggu Kang, Duksan Ryu, Jongmoon Baik. Predicting just‐in‐time software defects to reduce post‐release quality costs in the maritime industry. Software: Practice and Experience. 2020; 51 (4):748-771.

Chicago/Turabian Style

Jonggu Kang; Duksan Ryu; Jongmoon Baik. 2020. "Predicting just‐in‐time software defects to reduce post‐release quality costs in the maritime industry." Software: Practice and Experience 51, no. 4: 748-771.