When functions change their names: automatic detection of origin relationships

Kim, Sunghun; Pan, Kai; Whitehead, E. James

doi:10.1109/wcre.2005.33

Cited by 30 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visibility modifier, return type, but exception throws are not considered. According to [15], since a function's signature does not change frequently, the signature pattern can be used in determining similarity between methods. • Method Spread (MSP).…”

Section: Phase-2 Metrics Formulation and Clusteringmentioning

confidence: 99%

Aspect mining using Self-Organizing Maps with method level dynamic software metrics as input vectors

Maisikeli

Mitropoulos

2010

2010 2nd International Conference on Software Technology and Engineering

View full text Add to dashboard Cite

A major impediment to program comprehension, maintenance and evolvability is the existence of crosscutting concerns scattered across different modules tangled with implementations of other concerns. Presence of crosscutting concerns in software systems can lead to bloated and inefficient software systems that are difficult to evolve, hard to analyze, difficult to reuse and costly to maintain. This paper shows that clustering based on easily extractable software features derived through method calls, parameter sharing and method interactions represented as dynamic metrics can be used to determine code scattering and or tangling, thereby providing a steppingstone towards identifying crosscutting concerns leading to mining of possible aspect candidates. A three-step approach is used in the Aspect Mining methodology introduced in this paper. In the first step, two legacy programs were dynamically traced, and data representing interaction between code fragments were collected. In the second step, metrics were formulated from the collected data and submitted as input to Self Organizing Maps for clustering. In the third step, the obtained clusters were mapped against the test programs in order to identify code scattering and tangling symptoms, leading to identification of aspect candidates. Finally viable validation methodologies were applied to assess performance, and establish the validity of the methodologies used. Results obtained in this paper are found to have matched or exceeded results obtained in other existing Aspect Mining methods

show abstract

Section: Phase-2 Metrics Formulation and Clusteringmentioning

confidence: 99%

Aspect mining using Self-Organizing Maps with method level dynamic software metrics as input vectors

Maisikeli

Mitropoulos

2010

2010 2nd International Conference on Software Technology and Engineering

View full text Add to dashboard Cite

show abstract

“…Another line of research related to MkDiff is origin analysis ( 39), ( 40), (41). Kim et al (40) use various similarity factors for matching entities from one version to another, including methods' names/signatures, caller/callee sets, texts, and complexity metrics. To match two methods in two versions, BEAGLE (41) first computes internal similarity based on the metrics such as Cyclomatic, S-, and D-Complexity.…”

Section: Related Workmentioning

confidence: 99%