Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)
PurposeSoftware reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.Design/methodology/approachAs testing improves quality and reliability of the application by identif...
Gespeichert in:
| Veröffentlicht in: | International journal of intelligent unmanned systems 2020-05, Vol.8 (2), p.129-140 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 140 |
|---|---|
| container_issue | 2 |
| container_start_page | 129 |
| container_title | International journal of intelligent unmanned systems |
| container_volume | 8 |
| creator | D, Sudharson Dr, Prabha |
| description | PurposeSoftware reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.Design/methodology/approachAs testing improves quality and reliability of the application by identifying the bugs in it. Also, it defines the behavior and state of the product based on the defined principles and mechanisms. Conventional reliability models use statistical distributions to attain realistic features.FindingsThe ability to predict the bugs in the application during development phase itself is a proper testing practice which saves the time and increases the efficiency of the application. Efficient management and timely release of the product is based on this reliability testing and ant colony optimization (ACO)-based testing is an important optimization model which is available for testing the application.Originality/valueConventional ant colony optimization used test case generation as its common approach for testing the reliability of the application. ACO uses pheromone activity and it is related in testing of application and provides a simple positive mechanism by identifying the inactivity and precociousness. |
| doi_str_mv | 10.1108/IJIUS-09-2019-0052 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_emera</sourceid><recordid>TN_cdi_emerald_primary_10_1108_IJIUS-09-2019-0052</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2499019539</sourcerecordid><originalsourceid>FETCH-LOGICAL-c317t-a87cb623568afda88104424a5784ed0fa3eece27071b9997a04510d0c420fa0a3</originalsourceid><addsrcrecordid>eNptkc9Kw0AQxhdRsNS-gKcFL3qIzv5JNnss9U8rhRa052WTbHBLkq2bLRJPvoNv6JOYth4UPAwz8H3fDPwGoXMC14RAejN7nK2eIpARBSIjgJgeoQEFLqOEU3H8az5Fo7ZdAwARCWOpHCA97TJvC9y6Mrxpb7A3ldWZrWzocO0KU-E3G17wsteCw4Vtg7fZNljXYN0UfQWcu8o1HXabYGv7rvfa5fL26-NzPFlcnaGTUletGf30IVrd3z1PptF88TCbjOdRzogIkU5FniWUxUmqy0KnKQHOKdexSLkpoNTMmNxQAYJkUkqhgccECsg57UXQbIguDns33r1uTRvU2m19059UlEvZo4mZ7F304Mq9a1tvSrXxtta-UwTUjqba01Qg1Y6m2tHsQ-QQMrXxuir-z_z5APsGgNB3ng</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2499019539</pqid></control><display><type>article</type><title>Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)</title><source>Emerald Journals</source><source>Standard: Emerald eJournal Premier Collection</source><creator>D, Sudharson ; Dr, Prabha</creator><creatorcontrib>D, Sudharson ; Dr, Prabha</creatorcontrib><description>PurposeSoftware reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.Design/methodology/approachAs testing improves quality and reliability of the application by identifying the bugs in it. Also, it defines the behavior and state of the product based on the defined principles and mechanisms. Conventional reliability models use statistical distributions to attain realistic features.FindingsThe ability to predict the bugs in the application during development phase itself is a proper testing practice which saves the time and increases the efficiency of the application. Efficient management and timely release of the product is based on this reliability testing and ant colony optimization (ACO)-based testing is an important optimization model which is available for testing the application.Originality/valueConventional ant colony optimization used test case generation as its common approach for testing the reliability of the application. ACO uses pheromone activity and it is related in testing of application and provides a simple positive mechanism by identifying the inactivity and precociousness.</description><identifier>ISSN: 2049-6427</identifier><identifier>EISSN: 2049-6427</identifier><identifier>DOI: 10.1108/IJIUS-09-2019-0052</identifier><language>eng</language><publisher>Bingley: Emerald Publishing Limited</publisher><subject>Ant colony optimization ; Codes ; Debugging ; Failure ; Food ; Growth models ; Optimization ; Pheromones ; Reliability analysis ; Software development ; Software engineering ; Software quality ; Software reliability ; Statistical distributions</subject><ispartof>International journal of intelligent unmanned systems, 2020-05, Vol.8 (2), p.129-140</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c317t-a87cb623568afda88104424a5784ed0fa3eece27071b9997a04510d0c420fa0a3</citedby><cites>FETCH-LOGICAL-c317t-a87cb623568afda88104424a5784ed0fa3eece27071b9997a04510d0c420fa0a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/IJIUS-09-2019-0052/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,780,784,967,11635,21695,27924,27925,52689,53244</link.rule.ids></links><search><creatorcontrib>D, Sudharson</creatorcontrib><creatorcontrib>Dr, Prabha</creatorcontrib><title>Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)</title><title>International journal of intelligent unmanned systems</title><description>PurposeSoftware reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.Design/methodology/approachAs testing improves quality and reliability of the application by identifying the bugs in it. Also, it defines the behavior and state of the product based on the defined principles and mechanisms. Conventional reliability models use statistical distributions to attain realistic features.FindingsThe ability to predict the bugs in the application during development phase itself is a proper testing practice which saves the time and increases the efficiency of the application. Efficient management and timely release of the product is based on this reliability testing and ant colony optimization (ACO)-based testing is an important optimization model which is available for testing the application.Originality/valueConventional ant colony optimization used test case generation as its common approach for testing the reliability of the application. ACO uses pheromone activity and it is related in testing of application and provides a simple positive mechanism by identifying the inactivity and precociousness.</description><subject>Ant colony optimization</subject><subject>Codes</subject><subject>Debugging</subject><subject>Failure</subject><subject>Food</subject><subject>Growth models</subject><subject>Optimization</subject><subject>Pheromones</subject><subject>Reliability analysis</subject><subject>Software development</subject><subject>Software engineering</subject><subject>Software quality</subject><subject>Software reliability</subject><subject>Statistical distributions</subject><issn>2049-6427</issn><issn>2049-6427</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptkc9Kw0AQxhdRsNS-gKcFL3qIzv5JNnss9U8rhRa052WTbHBLkq2bLRJPvoNv6JOYth4UPAwz8H3fDPwGoXMC14RAejN7nK2eIpARBSIjgJgeoQEFLqOEU3H8az5Fo7ZdAwARCWOpHCA97TJvC9y6Mrxpb7A3ldWZrWzocO0KU-E3G17wsteCw4Vtg7fZNljXYN0UfQWcu8o1HXabYGv7rvfa5fL26-NzPFlcnaGTUletGf30IVrd3z1PptF88TCbjOdRzogIkU5FniWUxUmqy0KnKQHOKdexSLkpoNTMmNxQAYJkUkqhgccECsg57UXQbIguDns33r1uTRvU2m19059UlEvZo4mZ7F304Mq9a1tvSrXxtta-UwTUjqba01Qg1Y6m2tHsQ-QQMrXxuir-z_z5APsGgNB3ng</recordid><startdate>20200504</startdate><enddate>20200504</enddate><creator>D, Sudharson</creator><creator>Dr, Prabha</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KB.</scope><scope>L6V</scope><scope>L7M</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20200504</creationdate><title>Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)</title><author>D, Sudharson ; Dr, Prabha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c317t-a87cb623568afda88104424a5784ed0fa3eece27071b9997a04510d0c420fa0a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Ant colony optimization</topic><topic>Codes</topic><topic>Debugging</topic><topic>Failure</topic><topic>Food</topic><topic>Growth models</topic><topic>Optimization</topic><topic>Pheromones</topic><topic>Reliability analysis</topic><topic>Software development</topic><topic>Software engineering</topic><topic>Software quality</topic><topic>Software reliability</topic><topic>Statistical distributions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>D, Sudharson</creatorcontrib><creatorcontrib>Dr, Prabha</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Materials Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of intelligent unmanned systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>D, Sudharson</au><au>Dr, Prabha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO)</atitle><jtitle>International journal of intelligent unmanned systems</jtitle><date>2020-05-04</date><risdate>2020</risdate><volume>8</volume><issue>2</issue><spage>129</spage><epage>140</epage><pages>129-140</pages><issn>2049-6427</issn><eissn>2049-6427</eissn><abstract>PurposeSoftware reliability models in the past few years attracted researchers to build an accurate model in the software engineering. Testing is an important factor in the software development cycle.Design/methodology/approachAs testing improves quality and reliability of the application by identifying the bugs in it. Also, it defines the behavior and state of the product based on the defined principles and mechanisms. Conventional reliability models use statistical distributions to attain realistic features.FindingsThe ability to predict the bugs in the application during development phase itself is a proper testing practice which saves the time and increases the efficiency of the application. Efficient management and timely release of the product is based on this reliability testing and ant colony optimization (ACO)-based testing is an important optimization model which is available for testing the application.Originality/valueConventional ant colony optimization used test case generation as its common approach for testing the reliability of the application. ACO uses pheromone activity and it is related in testing of application and provides a simple positive mechanism by identifying the inactivity and precociousness.</abstract><cop>Bingley</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/IJIUS-09-2019-0052</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2049-6427 |
| ispartof | International journal of intelligent unmanned systems, 2020-05, Vol.8 (2), p.129-140 |
| issn | 2049-6427 2049-6427 |
| language | eng |
| recordid | cdi_emerald_primary_10_1108_IJIUS-09-2019-0052 |
| source | Emerald Journals; Standard: Emerald eJournal Premier Collection |
| subjects | Ant colony optimization Codes Debugging Failure Food Growth models Optimization Pheromones Reliability analysis Software development Software engineering Software quality Software reliability Statistical distributions |
| title | Hybrid software reliability model with Pareto distribution and ant colony optimization (PD–ACO) |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T20%3A03%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_emera&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Hybrid%20software%20reliability%20model%20with%20Pareto%20distribution%20and%20ant%20colony%20optimization%20(PD%E2%80%93ACO)&rft.jtitle=International%20journal%20of%20intelligent%20unmanned%20systems&rft.au=D,%20Sudharson&rft.date=2020-05-04&rft.volume=8&rft.issue=2&rft.spage=129&rft.epage=140&rft.pages=129-140&rft.issn=2049-6427&rft.eissn=2049-6427&rft_id=info:doi/10.1108/IJIUS-09-2019-0052&rft_dat=%3Cproquest_emera%3E2499019539%3C/proquest_emera%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2499019539&rft_id=info:pmid/&rfr_iscdi=true |