A Program Slicing-Based Bayesian Network Model for Change Impact Analysis

Ufuktepe, Ekincan; Tuğlular, Tuğkan

doi:10.1109/qrs.2018.00062

Cited by 9 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We measure the impact based on the impacted methods, where their probability is over 0.1. We have selected the 0.1 threshold due to the change impact analysis tool we used, and its related study [11], [28] has shown that using the threshold 0.1 provides higher f-measure and recall results. To calculate the impact rate, we divide the impacted methods by the total methods.…”

Section: Table V Selected Project and Bug Fix Informationmentioning

confidence: 99%

Tracking Code Bug Fix Ripple Effects Based on Change Patterns Using Markov Chain Models

2022

Self Cite

View full text Add to dashboard Cite

Change impact analysis evaluates the changes that are made in the software and finds the ripple effects, in other words, finds the affected software components. Code changes and bug fixes can have a high impact on code quality by introducing new vulnerabilities or increasing their severity. A recent high-visibility example of this is the code changes in the log4j web software CVE-2021-45105 to fix known vulnerabilities by removing and adding method called change types. This bug fix process exposed further code security concerns. In this article, we analyze the most common set of bug fix change patterns to have a better understanding of the distribution of software changes and their impact on code quality. To achieve this, we implemented a tool that compares two versions of the code and extracts the changes that have been made. Then, we investigated how these changes are related to change impact analysis. In our case study, we identified the change types for bug-inducing and bug fix changes using the Quixbugs dataset. Furthermore, we used 13 of the projects and 621 bugs from Defects4J to identify the common change types in bug fixes. Then, to find the change types that cause an impact on the software, we performed an impact analysis on a subset of projects and bugs of Defects4J. The results have shown that, on average, 90% of the bug fix change types are adding a new method declaration and changing the method body. Then, we investigated if these changes cause an impact or a ripple effect in the software by performing a Markov chain-based change impact analysis. The results show that the bug fix changes had only impact rates within a range of 0.4-5%. Furthermore, we performed a statistical correlation analysis to find if any of the bug fixes have a significant correlation with the impact of change. The results have shown that there is a negative correlation between caused impact with the change types adding new method declaration and changing method body. On the other hand, we found that there is a positive correlation between caused impact and changing the field type.

show abstract

Section: Table V Selected Project and Bug Fix Informationmentioning

confidence: 99%

Tracking Code Bug Fix Ripple Effects Based on Change Patterns Using Markov Chain Models

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous studies have used Bayesian Network (BN) to calculate change propagation [5] and perform change impact analysis [6], [7]. Even though that BNs are powerful probabilistic reasoning systems under uncertainty, they have very high complexity, and lack representing cycles.…”

Section: Introductionmentioning

confidence: 99%

“…Even though that BNs are powerful probabilistic reasoning systems under uncertainty, they have very high complexity, and lack representing cycles. For instance, a study [6] had to remove cycles (dependencies) to used BN, which might have caused to lose valuable information. Therefore, in our study we used Markov chain, which is commonly used in making predictions.…”

Section: Introductionmentioning

confidence: 99%

“…Program slicing is also used in change impact analysis [6], [8], [9]. Especially, forward slicing is well-known to be used for finding the affected components after a change.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this approach depends on the quality of the test cases, which could be a case where not all the mutants can be eliminated. Another study employed the diff between two consecutive versions as ground-truth and utilized the commits between the two consecutive versions for analysis [6]. In this study, we have used the diff of two consecutive versions on their bytecode, since that there could be change in due to the compiler.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Code Change Sniffer: Predicting Future Code Changes with Markov Chain

Ufuktepe

Tuğlular

2021

2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)

Self Cite

View full text Add to dashboard Cite

Code changes are one of the essential processes of software evolution. These changes are performed to fix bugs, improve quality of software, and provide a better user experience. However, such changes made in code could lead to ripple effects that can cause unwanted behavior. To prevent such issues occurring after code changes, code change prediction, change impact analysis techniques are used. The proposed approach uses static call information, forward slicing, and method change information to build a Markov chain, which provides a prediction for code changes in the near future commits. For static call information, we utilized and compared call graph and effect graph. We performed an evaluation on five open-source projects from GitHub that varies between 5K-26K lines of code. To measure the effectiveness of our proposed approach, recall, precision, and f-measure metrics have been used on five opensource projects. The results show that the Markov chain that is based on call graph can have higher precision compared to effect graph. On the other hand, for small number of cases higher recall values are obtained with effect graph compared to call graph. With a Markov chain model based on call graph and effect graph, we can achieve recall values between 98%-100%.

show abstract