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Dynamic Unit State Data-Driven False Alarm Filtering for Regression Unit Testing
Youngseok Choi, Ahcheong Lee, Hyoju Nam, Insub Lee, Namhoom Jung, Kyutae Cho, Moonzoo Kim
http://doi.org/10.5626/JOK.2024.51.11.996
Regression testing focuses on testing changed parts of software to quickly find errors caused by changes. Unit testing individually tests each unit (i.e., a small component of software) to identify a bug quickly. We propose a new regression testing technique using unit testing with a dynamic unit state-based false alarm reduction model. Experimental results showed that when the proposed technique was applied to 10 C programs, acc@10 performance increased by 40%p compared to the state-of-the-art technique foridentifying a buggy function. For 7 programs, target regression bugs were ranked within the top 20% of the bugs reported by the proposed technique.
A Study on Development Method for BERT-based False Alarm Classification Model in Weapon System Software Static Test
Hyoju Nam, Insub Lee, Namhoon Jung, Seongyun Jeong, Kyutae Cho, Sungkyu Noh
http://doi.org/10.5626/JOK.2024.51.7.620
Recently, as the size and complexity of software in weapon systems have increased, securing the reliability and stability is required. To achieve this, developers perform static and dynamic reliability testing during development. However, a lot of false alarms occur in static testing progress that cause wasting resources such as time and cost for reconsider them. Recent studies have tried to solve this problem by using models such as SVM and LSTM. However, they have a critical limitation in that these models do not reflect correlation between defect code line and other lines since they use Word2Vec-based code embedding or only code information. The BERT-based model learns the front-to-back relationship between sentences through the application of a bidirectional transformer. Therefore, it can be used to classify false alarms by analyzing the relationship between code. In this paper, we proposed a method for developing a false alarm classification model using a BERT-based model to efficiently analyze static test results. We demonstrated the ability of the proposed method to generate a dataset in a development environment and showed the superiority of our model.
A Study on Reduction of False Alarms in Weapon System Software Static Test Using Natural Language Processing Model
Insub Lee, Hyoju Nam, Namhoon Jung, Kyutae Cho, Sungkyu Noh
http://doi.org/10.5626/JOK.2024.51.3.244
Recently, Securing software stability has become increasingly important as military systems have been upgraded. To this end, the Defense Acquisition Program Administration conducts reliability tests for weapon system software through static analysis tools. However, many false alarms occurred during the test process, resulting in a waste of time and resources. This paper aims to achieve a high positive/false positive classification rate by creating a dataset using the log of a static analysis tool and training a language model. Additionally, data processing methods appropriate for the static analysis features of weapon system software were investigated and analyzed during the research. As a result of the analysis, it was found that the CodeBert model pretrained in C/CPP and natural language using Optuna, a hyperparameter tuning tool, showed 4-5% higher performance based on the F1 score than the existing SoTA model. If the model presented in this research is mainly employed in software static testing, a significant number of false positives can be found.
ILP-based Schedule Synthesis of Time-Sensitive Networking
Jin Hyun Kim, Hyonyoung Choi, Kyong Hoon Kim, Insup Lee, Se-Hoon Kim
http://doi.org/10.5626/JOK.2021.48.6.595
IEEE 802.1Qbv Time Sensitive Network (TSN), the latest real-time Ethernet standard, is a network designed to guarantee the temporal accuracy of streams. TSN is an Ethernet-based network system that is actively being developed for the factory automation and automobile network systems. TSN controls the flow of data streams based on schedules generated statically off-line to satisfy end-to-end delay or jitter requirements. However, the generation of TSN schedules is an NP-hard problem; because of this, constraint solving techniques, such as SMT (Satisfiability Modulo Theory) and ILP (Integer Linear Programming), have mainly been proposed as solutions to this problem. This paper presents a new approach using a heuristic greedy and incremental algorithm working with ILP to decrease the complexity of computing schedules and improve the schedule generation performance in computing TSN schedules. Finally, we compare our proposed method with the existing SMT solver approach to show the performance of our approach.
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