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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.
Design and Implementation of a Concurrency Error Detection Method for Embedded Software Using Machine Learning
Dongeon Lee, Jiwon Kim, Junghun Jin, Kyutae Cho
http://doi.org/10.5626/JOK.2022.49.5.327
Unlike general-purpose software, embedded software is designed by optimizing hardware for a specific purpose, so it is important to satisfy the target performance in a limited environment. Embedded software is increasing significantly in scale and complexity compared to the past. As the scale and complexity increase, the types of errors that occur in the software also diversify. Among them, there are many issues regarding concurrency errors that may occur between complex software modules. To detect concurrency errors in such embedded software, we have previously relied on manual input from the user. However, in this study, we propose a machine learning-based concurrency error detection tool (MCED) using SVM and deep learning.
Efficient Test Method for Embedded Software Using Next Software Framework Test Service
Dongeon Lee, Hyunggon Song, Junghun Jin, Kyutae Cho
http://doi.org/10.5626/JOK.2020.47.9.804
In recent years, it has become difficult to maintain the software quality and reliability of embedded software as its size and complexity have increased significantly unlike in the past. To improve such software quality and reliability, the most important factor is efficient testing of the software. Because of the nature of embedded software, it is difficult to apply existing test automation tools mainly used for Windows or Linux in general-purpose systems because of the high coupling with hardware and various platforms. In general, when developing software and hardware together, the number of hardware that can be operated is also minimal compared to the number of software developers. In this paper, we propose a method for the efficient testing of embedded software using NSFW(Next Software Framework) test service. Additionally, this paper suggests a method to test concurrency errors more efficiently.
Development of Reconfigurable Tactical Operation Display Framework by Battery and Battalion
Sangtae Lee, Seungyoung Lee, SoungHyouk Wi, Kyutae Cho
The tactical operation centers of future anti-aircraft missile systems provide the environment for the research on future air threats, tactical information, integrated battlefield environment creation and management, engagement control and command and control algorithms. To develop the key functional elements of integrated battlefield situation creation and processing and tactical operation automation processing operations, battery/battalion tactical operation control and reconfiguration design software are required. Therefore, the algorithm software of each function and the tactical operation display software and link software for interworking between equipment were developed as reconfigurable through a data-centric design. In this paper, a tactical operation display framework that can be reconfigured on the operation display of the tactical operations according to the battery/battalion is introduced. This tactical operation display framework was used to develop a common data model design for the reconfigurable structure of multi-role tactical operations with battery / battalion and mission views, and a display configuration tool that provides a tactical operation display framework for view development was also developed using the MVC pattern. If the tactical operation display framework is used, it will be possible to reuse the view design through the common base structure, and a view that can be reconfigured easily and quickly will also be developed.
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