Foforɔ:Nhyehyɛeɛ Mfonini 2.0Sua pii

ROI Nkontaabu

Bu mfaso a ɛwɔ automated validation so ho akontaa

Fa saa akontabuo yi yɛ akwankyerɛ akontabuo a ɛfa berɛ ne ɛka a ɛbɛba wɔ AI-driven test automation a wɔde bedi dwuma no ho. Wɔayɛ nea ebefi mu aba no ama nhyehyɛe ho nkɔmmɔbɔ a ɛfa emu nhyehyɛe ho.

Saa akontabuo yi de akontabuo a egyina nsusuiɛ a wɔtaa de di dwuma so ma. Nea efi mu ba ankasa no gu ahorow gyina wo tebea pɔtee, adwumayɛ nhyehyɛe, ne sɛnea wɔde di dwuma so.

Nneɛma a atwa wo ho ahyia

10
1100

Dwumadi ahorow anaa nhyehyɛe ahorow a ɛda nsow a wɔresɔ ahwɛ

2
120

Sɛ wɔkyekyem pɛpɛɛpɛ a, wɔ application ahorow nyinaa mu

4 hrs
1 hrs24 hrs

Wall-clock bere ma sɔhwɛ mmirikatu a edi mũ

25
5200

Engineering ne QA ti dodow

6
050

Regressions a ɛhwehwɛ sɛ wɔyɛ nhwehwɛmu

Ɛba fam: <30%, Mfinimfini: 30-70%, Ɔsoro: >70% a wɔde kata so

Nkɛntɛnso a wɔabu akontaa (akwankyerɛ) .

Potential value a egyina wo inputs so

Bere a wɔkora so ɔsram biara

80 hrs – 128 hrs

Nsaano sɔhwɛ mmɔdenbɔ a wɔtew so

Nkɛntɛnso a wɔabu akontaa sɛ ɛbɛba afe biara

$96K – $153K

Sɔhwɛ a wɔaka abom ne nsɛm a esisi a wɔtew so

Nneɛma foforo a ɛsɛ sɛ wususuw ho

Nsɛm a wɔde ma ntɛmntɛm kyinhyia

Nkɔso a ɛkɔ fam wɔ nsɛm a wɔde ma ho kyinhyia mu. AI-driven validation betumi ayɛ automation ne surface gaps a ɛwɔ hɔ dedaw no.

Asiane a ɛwɔ hɔ sɛ wɔayi no afi mu no so tew

Asiane a ɛwɔ hɔ sɛ wɔayi no adi no so tew kakraa bi. Coverage analysis boa ma wohu mmeae a anifuraefo wɔ wɔ mprempren sɔhwɛ suites mu.

Wopɛ sɛ wɔyɛ nhwehwɛmu a wɔayɛ ama wo?

Yɛn kuw no betumi ayɛ nhwehwɛmu a ɛkɔ akyiri a egyina wo tebea pɔtee ne wo botae ahorow so.

Nya nhwehwɛmu

Nneɛma a wɔyɛ no pefee

Ɔkwan a wɔfa so yɛ ne nsusuwii ahorow

Sɛ wote sɛnea wobu saa akontaabu yi ase a, ɛboa wo ma wohwɛ sɛnea wɔde bedi dwuma wɔ w’ahyehyɛde no mu.

Saa akontabuo yi bu akontaa sɛ nneɛma titire mmienu a ɛma ɛsom boɔ:

Sɔhwɛ bere a wɔtew so

Bere a wɔde yɛ sɔhwɛ no yɛ ne ne siesie a ɛso tew. Nea ɛka ho ne mmɔdenbɔ a wɔde sɔ hwɛ a wɔde afiri yɛ ne nea wɔde nsa yɛ nyinaa.

Asɛm a esisi ho ka a wɔatew so

Sika a wɔkora so fi regressions a wɔkyere ansa na wɔayɛ. Wɔsusuw no wɔ asɛm a esii ho mmuae a ɛso tew ne bere a wɔde siesie nneɛma mu.

Wɔakyerɛ nea ɛfiri mu baeɛ sɛ ranges de akyerɛ nsakraeɛ a ɛwɔ hɔ wɔ ahyehyɛdeɛ ahodoɔ ne dwumadie ahodoɔ mu.

Sɛnea wɔde saa akontaabu yi bedi dwuma

Saa akontabuo yi yɛ beaeɛ a wɔfiri aseɛ yɛ emu nkɔmmɔdie.

1

Fa nkɔmmɔbɔ no hyɛ nhyehyɛe mu

Kyɛ saa akontabuo yi ma wɔn a wɔdi dwuma no de kyerɛ mfasoɔ a ɛbɛtumi aba sɛ wɔbɛma sɔhwɛ afiri a wɔde yɛ adwuma no atu mpɔn.

2

Fa nsɛm a wɔde ahyɛ mu no yɛ nokware

Hwɛ nsusuwii ahorow no mu na sesa nsɛm a wɔde hyɛ mu no mu ma ɛda wo kuw no metrics ankasa adi yiye.

3

Nya nhwehwɛmu a wɔayɛ no sɛnea ɛfata

Wo ne Zof nkasa na woanya nhwehwɛmu a ɛkɔ akyiri a egyina wo tebea pɔtee ne wo botae ahorow so.

Nya ROI nhwehwɛmu a wɔayɛ ama wo

Ɛho nhia sɛ wode wo ho hyɛ mu. Yɛn kuw no bɛyɛ amanne nhwehwɛmu.

Nimdeɛ Gyinabea | Zof AI