Comprehending Scenario-Level Software Evolution Using Calling Context Trees

Gupta, Saurabh; Singh, Paramvir

doi:10.1109/icit.2017.33

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…The development of software cannot be separated from the evolution of software-"the dynamic behavior of software systems as they are maintained and enhanced over their lifetimes" [1,2]. In practical scenarios, software needs to be constantly updated to meet new user requirements and working environments [3,4], such as code modification, functionality increase, and bug fixes.…”

Section: Introductionmentioning

confidence: 99%

CRA: Identifying Key Classes Using Markov-Chain-Based Ranking Aggregation

Pan

Jiang

et al. 2022

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Researchers have proposed many approaches to identify key classes in software from the perspective of complex networks, such as CONN-TOTAL-W, PageRankBR, and ElementRank, which can effectively help developers understand software. However, these approaches tend to rely on a single metric when measuring the importance of classes. They do not consider the aggregation of multiple metrics to select the winner classes that rank high in majority metrics. In this work, we propose a key class identification approach using Markov-Chain-based ranking aggregation, namely CRA. First, CRA constructs a weighted directed class coupling network (WDCCNet) to describe the software and further applies existing approaches on WDCCNet to calculate class importance. Second, CRA filters out some metrics according to specific rules and uses the Markov chain to aggregate the remaining metrics. When the state probability distribution reaches a fixed point and does not change anymore, the classes in the software are sorted in a descending order according to the probability distribution, and the top-15% classes are treated as key classes. To evaluate the CRA approach, we compare it with 10 baseline approaches available on 6 pieces of software. Empirical results show that our approach is superior to the baselines according to the average ranking of the Friedman Test.

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Section: Introductionmentioning

confidence: 99%

CRA: Identifying Key Classes Using Markov-Chain-Based Ranking Aggregation

Pan

Jiang

et al. 2022

Axioms

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“…Software maintenance and evolution are crucial to the success of modern software development [1]. As the essential characteristic of software, software evolution is defined as the dynamic behavior of software systems.…”

Section: Introductionmentioning

confidence: 99%

COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation

Pan

Wang

et al. 2021

IEEE Access

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Software comprehension helps us to maintain software better. The developers often use documentation and mutual communication to understand software. However, the incompletion of documentation usually causes some problems such that we cannot acquire the detailed information of software. So researchers are now using key classes as starting points for software understanding and propose several effective methods for key class identification. Such methods are mostly implemented using supervised and unsupervised learning methods; however, they may be subject to class imbalance, insufficient amount of data, and lack of accuracy. In this paper, we propose a novel approach, called COSPA (identifying key Classes in Object-Oriented Software using Preference Aggregation), to identify key classes in software. Our approach is inspired by the social choice theory and uses Kemeny-Young method to find a preference that minimizes the total difference between different pairs of sequences returned by three key class identification methods (i.e., a-index, InDeg, and OSE). Further, we rank the classes in descending order according to that preference set, and a small number of top-ranked classes are considered as key candidates. To better evaluate the performance of our approach, we apply it to six Java software systems and compare it with nine state-ofthe-art approaches. In the experiments, we use Friedman to evaluate all methods in the whole set of research subjects; results show that, though no single method works always best in all subject systems, our method indeed performs better than others.INDEX TERMS Complex network, key classes, divergence, software comprehension.

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Comprehending Scenario-Level Software Evolution Using Calling Context Trees

Cited by 2 publications

References 20 publications

CRA: Identifying Key Classes Using Markov-Chain-Based Ranking Aggregation

CRA: Identifying Key Classes Using Markov-Chain-Based Ranking Aggregation

COSPA: Identifying Key Classes in Object-Oriented Software Using Preference Aggregation

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