Binciken Cibiyoyin Sadarwar Haɗin Sabis na Yanar Gizo ta Amfani da Ma'aunin Kama-Kama

1. Gabatarwa

Sabis na Yanar Gizo (WS) suna wakiltar sassan software masu cin gashin kansu da aka tsara don gano su daga nesa, kira su, da haɗa su. Yayin da hanyoyin ma'ana (misali, OWL-S) ke nufin yin tunani ta atomatik, amincewa da su yana da wahala saboda sarƙaƙiya da tsada. Saboda haka, tsarin samarwa ya fi dogaro da bayanin tsari ta amfani da WSDL (Yaren Bayanin Sabis na Yanar Gizo). Wannan binciken yana magance gibin ta hanyar bincika hanyoyin tsari don haɗin WS, musamman ta hanyar gina da binciken Cibiyoyin Sadarwar Haɗin Sabis na Yanar Gizo ta amfani da ma'aunin kama-kama na kirtani guda uku da aka kafa: Levenshtein, Jaro, da Jaro-Winkler. Babban manufa ita ce kwatanta aikin waɗannan ma'aunan wajen gano alaƙar sabis da ta dace bisa kaddarorin tsari kawai daga fayilolin WSDL na ainihi.

2. Bayanan Baya & Ayyukan Da Suka Gabata

2.1 Sabis na Yanar Gizo na Ma'ana da na Tsari

Tsarin sabis na yanar gizo na ma'ana, wanda aka yi amfani da ma'auni kamar OWL-S, yana neman saka ma'anar da na'ura za ta iya fassara a cikin bayanin sabis ta amfani da ilimin fahimta. Duk da haka, kamar yadda aka lura a cikin PDF kuma binciken daga Ƙungiyar Yanar Gizo ta Duniya (W3C) ya tabbatar, amincewa da su bai yadu ba saboda ƙoƙarin hannu mai mahimmanci da ake buƙata don bayanin kula da ƙalubalen da ba a warware su ba a cikin taswirar ilimin fahimta. Wannan matsalar aiki ta ci gaba da sha'awar ingantattun hanyoyin tsari waɗanda zasu iya aiki akan bayanan WSDL da ba na ma'ana ba, waɗanda suka zama mafi yawan sabis da aka tura.

2.2 Ma'aunin Kama-Kama don WSDL

Aikin da ya gabata kan gano tsari, kamar na [3] a cikin PDF, yana rarraba kamanceceniya tare da girma kamar na lexical (kaddarorin rubutu), sifa, mu'amala (sigogi na shigarwa/fitarwa na aiki), da QoS. Aikinmu ya mai da hankali kan matakan lexical da mu'amala, yana amfani da ma'aunin kama-kama na kirtani na gabaɗaya ga sunayen abubuwa (sabis, sunan aiki, sunayen sigogi) da aka ciro daga WSDL. Wannan hanya ta yi daidai da yanayin amfani da ma'anar ɓoyayye ta hanyar binciken rubutu na ƙididdiga, kamar yadda aka gani a hanyoyin kamar LSA (Binciken Ma'ana ta ɓoyayye) da ake amfani da su ga sabis na yanar gizo.

3. Hanyoyin Bincike & Gina Cibiyar Sadarwa

3.1 Tattara Bayanai & Gyara Su Kafin Aiki

An yi amfani da tarin bayanan WSDL na ainihi a matsayin wurin gwaji. An warware kowane fayil na WSDL don ciro mahimman abubuwa na tsari: sunayen sabis, sunayen aiki, da sunayen sigogi. An daidaita waɗannan abubuwan rubutu (ƙananan haruffa, cire haruffa na musamman) don samar da tushen lissafin kamanceceniya.

3.2 Aiwar Ma'aunin Kama-Kama

An aiwatar da ma'auni guda uku kuma aka kwatanta su:

Nisa na Levenshtein: Yana auna mafi ƙarancin adadin gyare-gyaren harafi ɗaya (shigarwa, sharewa, musanya) da ake buƙata don canza kirtani ɗaya zuwa wani. An lissafta kamanceceniyar da aka daidaita kamar $sim_{Lev}(s_1, s_2) = 1 - \frac{edit\_distance(s_1, s_2)}{\max(|s_1|, |s_2|)}$.
Kama-Kama na Jaro: Ya dogara ne akan adadin da tsarin haruffan da suka yi daidai. Tsarin lissafin shine $sim_j = \begin{cases} 0 & \text{idan } m=0 \\ \frac{1}{3}\left(\frac{m}{|s_1|} + \frac{m}{|s_2|} + \frac{m-t}{m}\right) & \text{in ba haka ba} \end{cases}$, inda $m$ shine adadin haruffan da suka yi daidai kuma $t$ shine rabin adadin musanyawa.
Kama-Kama na Jaro-Winkler: Bambance-bambancen da ke haɓaka maki don kirtani tare da ginshiƙan gaba ɗaya. $sim_{jw} = sim_j + (l \cdot p \cdot (1 - sim_j))$, inda $l$ shine tsayin ginshiƙan gaba ɗaya (har zuwa haruffa 4) kuma $p$ shine ma'auni mai daidaitawa (yawanci 0.1).

3.3 Tsarin Samar da Cibiyar Sadarwa

Ga kowane ma'auni, an gina Cibiyar Sadarwar Haɗin Sabis na Yanar Gizo. Nodes suna wakiltar sabis na yanar gizo ɗaya. An ƙirƙiri gefen da ba a jagorance ba tsakanin nodes na sabis biyu idan jimillar makin kamanceceniyar abubuwan da aka ciro (misali, matsakaicin kamanceceniya a duk nau'ikan sunayen aiki) ya wuce matakin da aka ƙayyade $ heta$. An samar da cibiyoyin sadarwa don kewayon ƙimar $ heta$ don bincika hankali.

4. Sakamakon Gwaji & Bincike

Taƙaitaccen Aiki Mai Muhimmanci

Jaro-Winkler ya gano ƙarin haɗin kai na ma'ana a manyan matakan maƙasudi. Jaro ya samar da cibiyoyin sadarwa masu yawa, waɗanda za su iya zama madaidaici a ƙananan matakan maƙasudi. Levenshtein ya kasance mafi hankali ga ƙananan bambance-bambancen rubutu.

4.1 Kwatanta Kaddarorin Tsari

An bincika tsarin tsari na cibiyoyin sadarwar da aka samar ta amfani da ma'auni kamar matsakaicin digiri, ma'aunin tari, da tsayin matsakaicin hanya. Cibiyoyin sadarwa da aka gina tare da Jaro-Winkler koyaushe suna nuna haɗin kai mafi girma (matsakaicin digiri mafi girma) da ƙarfin tari na gida a matakan maƙasudi masu kama, yana nuna cewa yana haɗa sabis tare da ayyuka masu kamanceceniya da gaske cikin inganci.

Bayanin Chati (Tunani): Chati na layi wanda ke nuna "Yawan Cibiyar Sadarwa" da "Matsakanin Kamanceceniya" don ma'auni uku zai nuna Jaro-Winkler yana kiyaye yawa mafi girma fiye da Jaro da Levenshtein yayin da matakin maƙasudi ke ƙaruwa, yana nuna ikonsa na riƙe haɗin kai mai ma'ana a ƙarƙashin ƙa'idodi masu tsauri.

4.2 Aikin Ma'auni a Matsakanin Maƙasudi Daban-daban

Binciken ya gano ciniki mai bayyanawa:

Manyan Matsakanin Maƙasudi ($\theta > 0.9$): Jaro-Winkler ya fi sauran su, har yanzu yana samar da haɗin kai na sabis masu alaƙa, yayin da sauran suka rabu. Wannan ya yi daidai da tsarinsa don daidaita sunaye da alamomi tare da ginshiƙan gaba ɗaya.
Ƙananan zuwa Matsakaicin Matsakanin Maƙasudi ($\theta \approx 0.7$): Ma'aunin Jaro ya fi dacewa, saboda ya samar da ƙananan gefuna na ƙarya (kuskuren gaskiya) idan aka kwatanta da Levenshtein, wanda sau da yawa yana haɗa sabis bisa ga jujjuyawar kirtani maras muhimmanci.

4.3 Gwajin Muhimmancin Ƙididdiga

Gwaje-gwajen ƙididdiga na bi da bi (misali, gwajin matsayi mai sanya hannu na Wilcoxon) akan rarraba ma'aunin cibiyar sadarwa a cikin samfuran bootstrap da yawa sun tabbatar da cewa bambance-bambancen a matsakaicin ma'aunin tari da tsakiyar digiri tsakanin Jaro-Winkler da sauran ma'aunan sun kasance masu mahimmanci a ƙididdiga ($p < 0.05$).

5. Tsarin Fasaha & Cikakkun Bayanan Lissafi

Jigon binciken ya dogara ne akan tsarin lissafi na ma'aunan. Jaro-Winkler mai haɓaka mahimmanci ne: $sim_{jw} = sim_j + (l \cdot p \cdot (1 - sim_j))$. Wannan yana ba da nauyi mai yawa ga daidaitawar ginshiƙi, wanda yake da tasiri sosai ga nomenclature na fasaha (misali, "getUserProfile" da "getUserData"). Sabanin haka, nisan gyara na Levenshtein, $d_{Lev}$, yana ɗaukar duk gyare-gyaren harafi daidai, yana mai da shi ƙaramin fahimta ga camelCase ko gajartattun sharuɗɗan da aka saba a cikin ƙirar API. Zaɓin aikin tarawa (matsakaici, max, matsakaicin nauyi) don haɗa kamanceceniya a cikin abubuwan sabis da yawa shima yana tasiri sosai ga nauyin gefe na ƙarshe da tsarin cibiyar sadarwa.

6. Nazarin Lamari: Yanayin Haɗin Sabis

Lamari: Ba da shawarar sarkar haɗin kai ta atomatik don sabis na "Yin Rajista na Tafiya" ta amfani da bayanan WSDL na tsari kawai.

Aiwatar Tsarin:

Wakilcin Node: Sabis: FlightSearch, HotelFinder, CarRentalAPI, WeatherService, CurrencyConverter.
Lissafin Kamanceceniya: Ta amfani da Jaro-Winkler, FlightSearch da HotelFinder suna da kamanceceniya mai girma saboda sunayen sigogi iri ɗaya kamar "location," "date," "adults." CarRentalAPI shima yana da maki masu girma tare da waɗannan. WeatherService da CurrencyConverter suna nuna ƙaramin kamanceceniya ga ƙungiyar jigon.
Samuwar Cibiyar Sadarwa: A matakin maƙasudi na 0.85, wani tari mai bayyanawa ya fito yana haɗa FlightSearch, HotelFinder, da CarRentalAPI.
Ƙididdigar Haɗin kai: Tarin cibiyar sadarwa kai tsaye yana ba da shawarar hanya mai yuwuwar haɗin kai: Sarka FlightSearch -> HotelFinder -> CarRentalAPI don cikakken aikin yin rajista na tafiya, tare da WeatherService da CurrencyConverter a matsayin sabis na gefe masu yuwuwa.

Wannan yana nuna yadda cibiyoyin sadarwar tsari zasu iya jagorantar gano haɗin kai ba tare da bayanin kula na ma'ana ba.

7. Ayyukan Gaba & Jagororin Bincike

Tsarin Haɗin Ma'ana da Tsari: Yin amfani da cibiyoyin sadarwar tsari a matsayin sauri, Layer na tacewa mai iya faɗaɗawa don rage ƙanƙara 'yan takara don ƙarin tunani na ma'ana mai tsada, kama da yadda sake dawowa-ƙarfafa samarwa ke aiki a cikin LLMs.
Haɗin kai tare da Taswirar Ilimin API: Saka nodes daga cibiyoyin sadarwar tsari cikin manyan taswirar ilimin API kamar waɗanda aka bincika a cikin binciken APIGraph, wadata su da gefuna na kamanceceniyar tsari.
Haɗin kai mai ƙarfi a cikin Microservices: Yin amfani da waɗannan samfuran cibiyar sadarwa ga yanayin aiki (misali, Kubernetes, Istio) don ba da shawara ko haɗa microservices ta atomatik bisa bayanan sallama na ainihin lokaci.
Ma'auni na Ci Gaba: Bincika kamanceceniya mai tushen saka (misali, amfani da BERT ko Word2Vec akan rubutun WSDL) don ɗaukar ma'ana mai zurfi na mahallin yayin riƙe "tsari" a ma'anar rashin buƙatar ilimin fahimta na yau da kullun.

8. Nassoshi

W3C. (2001). Yaren Bayanin Sabis na Yanar Gizo (WSDL) 1.1. Bayanin kula na W3C. An samo daga https://www.w3.org/TR/wsdl
Martin, D., et al. (2004). OWL-S: Alamar Ma'ana don Sabis na Yanar Gizo. Ƙaddamar da Memba na W3C.
Dong, X., et al. (2004). Binciken Kamanceceniya don Sabis na Yanar Gizo. A cikin Proceedings of the 30th VLDB Conference.
Elgazzar, K., et al. (2010). Rarraba Takaddun WSDL don Fara Gano Sabis na Yanar Gizo. A cikin IEEE International Conference on Web Services (ICWS).
Zhu, J., et al. (2020). APIGraph: Babban Taswirar Ilimin API. A cikin Proceedings of the 28th ACM Joint Meeting on ESEC/FSE.
Winkler, W. E. (1990). Ma'aunin Kwatanta Kirtani da Ƙarfafa Ƙa'idodin Yanke Shawara a cikin Samfurin Haɗin Rikodin Fellegi-Sunter.

9. Binciken Kwararru & Fahimta Mai Muhimmanci

Fahimta ta Jigo: Wannan takarda tana ba da duban gaskiya na aiki, mai buƙata. Ya gano daidai cewa babban hangen nesa na cikakken ma'ana, sabis na yanar gizo da aka haɗa ta atomatik ya tsaya cikin samarwa saboda sarƙaƙiya, yana maimaita matsalar "gibin amincewa" da ake gani a wasu fagagen da AI ke jagoranta. Juya masu rubutun zuwa ingantaccen kimanta hanyoyin tsari ba mataki ne na baya ba, amma wani mataki na dabarun gefe zuwa hanyoyin warwarewa masu yiwuwa. Aikin su da gaske yana jayayya: kafin mu iya koyar da injina "fahimtar" sabis, bari mu fara inganta yadda suke "gani" da "haɗa" su bisa tsarin saman. Wannan yana tunawa da farkon hanyoyin hangen nesa na kwamfuta masu tasiri sosai waɗanda suka dogara da siffofi da aka ƙera da hannu (kamar SIFT) kafin juyin juya halin koyo mai zurfi—sun yi aiki da ƙarfi tare da ƙarancin bayanai.

Kwararar Hankali: Hankali yana da inganci kuma yana mai da hankali kan injiniya. Premise: Hanyoyin ma'ana suna da tsada. Lura: Bayanan tsari (WSDL) suna da yawa. Hasashe: Ma'aunin kama-kama na kirtani daban-daban zasu samar da cibiyoyin sadarwar haɗin kai iri-iri. Gwaji: Gina cibiyoyin sadarwa, bincika tsari. Gano: Jaro-Winkler ya fi kyau don haɗin kai masu aminci mai girma; Jaro ya fi kyau don bincike mai faɗi, mai hayaniya. Kwararar daga gane matsalar ta hanyar kwatanta hanyoyin zuwa jagora mai aiki yana bayyananne kuma mai jan hankali.

Ƙarfi & Kurakurai: Babban ƙarfin shine aiwatar da dabarun kimiyyar cibiyar sadarwa ga matsalar injiniyan software, yana ba da hangen nesa na ƙididdiga, tsari akan alaƙar sabis. Amfani da fayilolin WSDL na ainihi ya kafa binciken cikin aiki. Duk da haka, babban aibi shine rashin gaskiyar ƙasa ta ƙididdiga don tabbatarwa. Ta yaya muka san cewa haɗin kai a cikin cibiyar sadarwa ya "dace"? Kima da alama wani ɓangare na hankali ne. Binciken zai ƙarfafa sosai ta hanyar kimanta cibiyoyin sadarwa da ma'auni na sanannun, ingantattun haɗin sabis ko amfani da cibiyoyin sadarwa don ƙarfafa mai ba da shawarar haɗin kai da auna daidaitonsa, kama da yadda ake kimanta hasashen haɗin kai a cikin binciken cibiyar sadarwar zamantakewa.

Fahimta Mai Aiki: Ga masu aiki, saƙon yana bayyananne: Fara da Jaro-Winkler. Idan kuna gina rajistar sabis ko tsarin ba da shawara kuma kuna buƙatar nemo sabis masu kamanceceniya sosai (misali, don kawar da kwafi ko shawarwari masu daidaito), aiwatar da Jaro-Winkler tare da babban matakin maƙasudi. Don ayyukan bincike, kamar gano sabis masu alaƙa masu yuwuwa a cikin yankuna, yi amfani da ma'aunin Jaro tare da ƙananan matakin maƙasudi. Binciken shima a ɓoye yana ba da shawarar dabarar ma'auni da yawa: yi amfani da ma'auni daban-daban a matakai daban-daban na bututun gano. Bugu da ƙari, wannan aikin ya kafa tushen ɗaukar yanayin sabis a matsayin jadawali—ra'ayi wanda shine tushe ga zamani na DevOps da injiniyan dandamali, kamar yadda aka gani a haɓakar kayan aiki kamar Backstage na Spotify, wanda ke amfani da katalojin software da aka ƙirƙira a matsayin jadawali. Mataki na gaba na hankali shine haɗa waɗannan gefuna na kamanceceniyar tsari cikin irin waɗannan ƙofofin masu haɓakawa don ba da shawarar dogaro da haɗin kai ta atomatik.