Code Change Sniffer: Predicting Future Code Changes with Markov Chain

Ufuktepe, Ekincan; Tuğlular, Tuğkan

doi:10.1109/compsac51774.2021.00137

Cited by 4 publications

(18 citation statements)

References 19 publications

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“…We aim to provide a better understanding of bug fix preference, due to its low impact. To evaluate the impact of the bug fixes, we use a Markov chain and program slicing-based impact analysis tool called Code Change Sniffer [11]. 3 Furthermore, we have extended our previous work [12], and provided a novel approach to detect change types between two commits that cause an impact on software components, while other studies [13]- [15] focus on statement-level (fine-grained) changes.…”

Section: ) Rq2: Is There a Relationship Between The Bug Fix Changementioning

confidence: 99%

“…Code Change Sniffer [11] is a change impact analysis tool that uses the Markov chain to calculate the probabilities of impacted methods in the software. The probabilistic information is computed by analyzing the software with static analysis.…”

Section: B Running Example Of Code Change Sniffermentioning

confidence: 99%

“…For example, the change can be calculated based on the lines of source codes, or for Java it could be the number of statements in bytecode. Even though the tool [11] we used is for quantifying the percentage of change is based on bytecode; to simplify the example, we will use lines of Java source code. In the running example given in Fig.…”

Section: B Running Example Of Code Change Sniffermentioning

confidence: 99%

“…analysis [11]. The change impact analysis approach uses forward slicing for data dependency to find affected statements after a change and uses CG information to find the dependency relationship between methods.…”

Section: Table V Selected Project and Bug Fix Informationmentioning

confidence: 99%

“…In this section, we provide the essential background to provide a better understanding of our study. In the following section, we provide a running example of the change impact analysis technique and tool called Code Change Sniffer [11], which we used in our study.…”

Section: Introductionmentioning

confidence: 99%

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

2022

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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.

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Section: ) Rq2: Is There a Relationship Between The Bug Fix Changementioning

confidence: 99%

Section: B Running Example Of Code Change Sniffermentioning

confidence: 99%

Section: B Running Example Of Code Change Sniffermentioning

confidence: 99%

Section: Table V Selected Project and Bug Fix Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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Application of the Law of Minimum and Dissimilarity Analysis to Regression Test Case Prioritization

Ufuktepe

Tuğlular

2023

IEEE Access

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Regression testing is one of the most expensive processes in testing. Prioritizing test cases in regression testing is critical for the goal of detecting the faults sooner within a large set of test cases. We propose a test case prioritization (TCP) technique for regression testing called LoM-Score inspired by the Law of Minimum (LoM) from biology. This technique calculates the impact probabilities of methods calculated by change impact analysis with forward slicing and orders test cases according to LoM. However, this ordering doesn't consider the possibility that consecutive test cases may be covering the same methods repeatedly. Thereby, such ordering can delay the time of revealing faults that exist in other methods.To solve this problem, we enhance the LoM-Score TCP technique with an adaptive approach, namely with a dissimilarity-based coordinate analysis approach. The dissimilarity-based coordinate analysis uses Jaccard Similarity for calculating the similarity coefficients between test cases in terms of covered methods and the enhanced technique called Dissimilarity-LoM-Score (Dis-LoM-Score) applies a penalty with respective on the ordered test cases. We performed our case study on 10 open-source Java projects from Defects4J, which is a dataset of real bugs and an infrastructure for controlled experiments provided for software engineering researchers. Then, we hand-seeded multiple mutants generated by Major, which is a mutation testing tool. Then we compared our TCP techniques LoM-Score and Dis-LoM-Score with the four traditional TCP techniques based on their Average Percentage of Faults Detected (APFD) results.INDEX TERMS Change impact analysis, regression testing, software testing, test case prioritization.

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Detecting Software Code Vulnerabilities Using 2D Convolutional Neural Networks with Program Slicing Feature Maps

Watson

Ufuktepe

Palaniappan

2022

2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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Code Change Sniffer: Predicting Future Code Changes with Markov Chain

Cited by 4 publications

References 19 publications

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

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

Application of the Law of Minimum and Dissimilarity Analysis to Regression Test Case Prioritization

Detecting Software Code Vulnerabilities Using 2D Convolutional Neural Networks with Program Slicing Feature Maps

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