On the evaluation of the Bunch search-based software modularization algorithm
Abstract The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mitchell, Brian S. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer-Verlag, 1997, 12(2007), 1 vom: 07. Juni, Seite 77-93 |
|---|---|
| Übergeordnetes Werk: |
volume:12 ; year:2007 ; number:1 ; day:07 ; month:06 ; pages:77-93 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-007-0218-3 |
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OLC2034866398 |
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10.1007/s00500-007-0218-3 doi (DE-627)OLC2034866398 (DE-He213)s00500-007-0218-3-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Mitchell, Brian S. verfasserin aut On the evaluation of the Bunch search-based software modularization algorithm 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the source code into abstract structures not in the source code called subsystems. The clustering process uses evolutionary algorithms to search through the enormous set of possible graph partitions, and is guided by a fitness function designed to measure the quality of individual graph partitions. The second part of this paper focuses on evaluating the results produced by our clustering technique. Our previous research has shown through both qualitative and quantitative studies that our clustering technique produces good results quickly and consistently. In this part of the paper we study the underlying structure of the search space of several open source systems. We also report on some interesting findings our analysis uncovered by comparing random graphs to graphs representing real software systems. Reverse engineering Software clustering Search algorithms Mancoridis, Spiros aut Enthalten in Soft computing Springer-Verlag, 1997 12(2007), 1 vom: 07. Juni, Seite 77-93 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:12 year:2007 number:1 day:07 month:06 pages:77-93 https://doi.org/10.1007/s00500-007-0218-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_95 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 12 2007 1 07 06 77-93 |
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10.1007/s00500-007-0218-3 doi (DE-627)OLC2034866398 (DE-He213)s00500-007-0218-3-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Mitchell, Brian S. verfasserin aut On the evaluation of the Bunch search-based software modularization algorithm 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the source code into abstract structures not in the source code called subsystems. The clustering process uses evolutionary algorithms to search through the enormous set of possible graph partitions, and is guided by a fitness function designed to measure the quality of individual graph partitions. The second part of this paper focuses on evaluating the results produced by our clustering technique. Our previous research has shown through both qualitative and quantitative studies that our clustering technique produces good results quickly and consistently. In this part of the paper we study the underlying structure of the search space of several open source systems. We also report on some interesting findings our analysis uncovered by comparing random graphs to graphs representing real software systems. Reverse engineering Software clustering Search algorithms Mancoridis, Spiros aut Enthalten in Soft computing Springer-Verlag, 1997 12(2007), 1 vom: 07. Juni, Seite 77-93 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:12 year:2007 number:1 day:07 month:06 pages:77-93 https://doi.org/10.1007/s00500-007-0218-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_95 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 12 2007 1 07 06 77-93 |
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10.1007/s00500-007-0218-3 doi (DE-627)OLC2034866398 (DE-He213)s00500-007-0218-3-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Mitchell, Brian S. verfasserin aut On the evaluation of the Bunch search-based software modularization algorithm 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2007 Abstract The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the source code into abstract structures not in the source code called subsystems. The clustering process uses evolutionary algorithms to search through the enormous set of possible graph partitions, and is guided by a fitness function designed to measure the quality of individual graph partitions. The second part of this paper focuses on evaluating the results produced by our clustering technique. Our previous research has shown through both qualitative and quantitative studies that our clustering technique produces good results quickly and consistently. In this part of the paper we study the underlying structure of the search space of several open source systems. We also report on some interesting findings our analysis uncovered by comparing random graphs to graphs representing real software systems. Reverse engineering Software clustering Search algorithms Mancoridis, Spiros aut Enthalten in Soft computing Springer-Verlag, 1997 12(2007), 1 vom: 07. Juni, Seite 77-93 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:12 year:2007 number:1 day:07 month:06 pages:77-93 https://doi.org/10.1007/s00500-007-0218-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_95 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 12 2007 1 07 06 77-93 |
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Abstract The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the source code into abstract structures not in the source code called subsystems. The clustering process uses evolutionary algorithms to search through the enormous set of possible graph partitions, and is guided by a fitness function designed to measure the quality of individual graph partitions. The second part of this paper focuses on evaluating the results produced by our clustering technique. Our previous research has shown through both qualitative and quantitative studies that our clustering technique produces good results quickly and consistently. In this part of the paper we study the underlying structure of the search space of several open source systems. We also report on some interesting findings our analysis uncovered by comparing random graphs to graphs representing real software systems. © Springer-Verlag 2007 |
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Abstract The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. This process is based on clustering the graph representing the modules and module-level dependencies found in the source code into abstract structures not in the source code called subsystems. The clustering process uses evolutionary algorithms to search through the enormous set of possible graph partitions, and is guided by a fitness function designed to measure the quality of individual graph partitions. The second part of this paper focuses on evaluating the results produced by our clustering technique. Our previous research has shown through both qualitative and quantitative studies that our clustering technique produces good results quickly and consistently. In this part of the paper we study the underlying structure of the search space of several open source systems. We also report on some interesting findings our analysis uncovered by comparing random graphs to graphs representing real software systems. © Springer-Verlag 2007 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2034866398</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502111511.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2007 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-007-0218-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2034866398</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00500-007-0218-3-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mitchell, Brian S.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On the evaluation of the Bunch search-based software modularization algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag 2007</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The first part of this paper describes an automatic reverse engineering process to infer subsystem abstractions that are useful for a variety of software maintenance activities. 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