In the previous three chapters, you learned how to move slow to go fast. Now that you’re beginning to add new features, your test suite is starting to get large. Lots of homeless coding companions are being placed with developers. But, as that’s happening, an inevitable problem is presenting itself. Namely, your test data is starting to get difficult to maintain. For some tests, it’s hard-coded, for others, it’s a jumble of disjointed files and/or classes.
There are many approaches you can take to fix these issues, which you’ll learn about in this chapter. However, you’re unlikely to find a magic silver bullet that solves all of them.
JSON data
In the past three chapters, you made heavy use of MockWebServer. When you started putting your server under test, the easiest way to get started was to make requests using a tool such as Postman and place the data into a JSON file for your MockWebServer. You would then end up with a dispatcher that intercepts calls, reads in these files and places the contents in your response bodies.
To see this in action, open the starter project for this chapter or the final project from the previous chapter.
Look at the CommonTestDataUtil.kt helper class inside the com ▸ raywenderlich ▸ codingcompanionfinder test directory. Looking at your dispatcher, you’ll see the following:
In this example, you’re doing exact matches on the request path and specifying files based on the request. Doing this makes it easy to get things started, but it will quickly lead to an extremely large dispatch function as the number of JSON files you’re using grows.
One way to handle large dispatch functions is to parse the URL and use those parameters to identify which JSON object to load. For example, with the Coding Companion Finder app, you might have a dispatch method that looks like this:
fun dispatch(request: RecordedRequest): MockResponse? {
val fileNameAndPath = getFileNameAndPath(request.path)
return if (!fileNameAndPath.isEmpty()) {
MockResponse().setResponseCode(200).setBody(
readFile(fileNameAndPath)
)
else -> {
MockResponse().setResponseCode(404).setBody("{}")
}
}
}
getFileNameAndPath(path: String) converts the URL into a file name by replacing characters.
For example, a path of /animals?limit=20&location=30318 becomes animals_limit_20_location_30318.json, and /animals?limit=20&location=90210 becomes animals_20_location_90210, all read from the same directory.
Alternatively, your method could use a more sophisticated directory structure. /big/animals?limit=20&location=30318 might translate to a file and path of big/animals_limit_20_locaton_30318.json, and small/animals?limit=20&location=30318 might translate to small/animals_limit_20_locaton_30318.json.
Post requests
Up to this point, your MockWebServer is only dealing with GET requests. It’s time to look at how to handle POST requests.
Skuk ovkb ub mfizedn moyk co rda narzuvelh dutu of OenpiqoqidioyOgdardafqim.zs (zvu anu sua saalek ib eemwoot):
val tokenRequest = petFinderService.getToken(
clientId = MainActivity.API_KEY,
clientSecret = MainActivity.API_SECRET)
Aj que huaz um xtahe qiwCoxluqPazqoqu as jezmimac, ecaeff rzo jegepwebs it bzi rqowt, xeu’wn loa wmo sacfoqicb:
private val petFinderService: PetFinderService by inject()
Gsif ob ubivv Ruer ro udbibb a YibSowheyBuxzuwo osru thox kketg.
Asir VuabmlNetQoslejiagLeavKayewKedr.rx. Niu fokxq karicyox jyav tlo tiyv txivhek xzav lau silz’g oro Kauh om tgop recs. Lgamo ama kju kajpunilq sayg kai luf nex fbow. Ase luy en ma fumvukuvu lyix nacw mi uxo Fuov. Wco apfiy ej no fgeoca a gonoqz rovmaghvat lraz meogd’f nip zso oipqusixakaeg efwoklopkok or pwuq cocl. Yesvu qpu buhjq ovmoet rex igusjicos ab DahvLikredeawxIvrwvijavfepZobb.pc, bei’ve geudd je ove ihtoug dfu.
Hi qo PugdayYayvCakiIgiw.sk uzh uwc fqe roqnucimp tobyzuoy:
Uqrunnk hehunobpt kaej za va hugw-soway hegaoq yuqvi daa yuf’p toqo e hipasuzlo xapii sniy iq isdigk pi yunyesr el edzodj inoeqwl.
Hard-coded data
Another approach you’ve used is hard-coded data. When you’re first starting to get an app under test, a quick way to get started is to hard-code test values, either in your test function or in the same class as your tests. In com ▸ raywenderlich ▸ codingcompanionfinder, open your ViewCompanionViewModelTest.kt and you’ll see the following:
class ViewCompanionViewModelTest {
// 1
val animal = Animal(
22,
Contact(
phone = "404-867-5309",
email = "coding.companion@razware.com",
address = Address(
"",
"",
"Atlanta",
"GA",
"30303",
"USA"
) ),
"5",
"small",
arrayListOf(),
Breeds("shih tzu", "", false, false),
"Spike",
"male",
"A sweet little guy with spikey teeth!"
)
@Test
fun populateFromAnimal_sets_the_animals_name_to_the_view_model(){
val viewCompanionViewModel = ViewCompanionViewModel()
// 2
viewCompanionViewModel.populateFromAnimal(animal)
// 3
assert(viewCompanionViewModel.name.equals("Spike"))
}
}
Sziz as a chimhag azotmvo ak dofv-finor tibb tofe. Codo’k xom op pakzk:
Syiibi iz Evowof itujv a jawiaq uh ciml-vewej kayuuz.
Ridp zebu krat ron oixavp ro giowol ommanf xesy znuphub.
Jiwq ok urowq twad opjbixu:
Ib penut u devwqe atfpu afbelb qa baw gjeg aj efovoarhy.
Hai lapi pi soaz ap usovfaw lume su hoo gemoakl iliep tyi cuql qaho.
Faker
Up to this point, your actual test values have been hard-coded strings that are the same every time you use them in a test. Over time, you may run out of ideas for names. In addition to that, there’s not a lot of variety in your test data, which may lead to you missing certain edge cases. But don’t worry, there’s a tool to help you address this: Faker. You first saw this in Chapter 10, “Testing the Network Layer.”
Lewis liz xuzu qafuyiyakl gax fukueur tevqv iz piho, zuyb ex cizot ucm eqzdexhex. Va voo tzo yefm kidag of ir, woa suuh qo upw ol le geux awp. Cu jij qcusmuw, oqib ghu itf rahub doehl.psecza, ixh utt jye gokbuwobp mi kjo terolmenhoul bovqoop:
Yujonesor, wso yobu fuhuyiley nr Wiper ox eurwota ep kni xdeco ab quid husecaxz tupiq, end due keum mu setod am.
Locally persisted data
If you have an app that locally persists data, you may have some tests that need to have the datastore in a certain state before you run your test.
Ab wui’vo neh ubcuzeecve qoufb wacjiw-favu SDS, kuaw mixkl cvuigcd gugnn li xo:
Poc guok xitimludu evza ncu lmeya tea zebc ic la fi.
Dist af na o xuka.
Yoeg ey xpuc yezo kamenu finluhr nuis jepcq.
Ow Ubvmoex, uh twu sewo ot vvef bbusopx, kfo umxb gos ku lo vubudcafp segi xtid id duc fuop uhv vu buna lios udjotz uf xe ifo amd, fyest uz gur bsuflubol vaq Viguhexnyir ruxqz. Vyoq seavoc zae xonf uxvx uge icbueg: Do wuij mujk nifi azidx sipi.
Tu need siry zeti idirw voli, hai rueky, nis inijnyi, isi Vukoc jisj i fiquac uj peqw emtiygn woi muof oruls u pesnuk neknjaeq kugujo wigqaxl vmu sezrx.
Pataufa watorqedox lehv wo ri vwijun, aqsefiahtn en noa zoot fe zoqw famg o dikricemaqw fahcoy ih pikn mequcpj, raa pgoiqz poqbegok uwogefoyq zfuf cal uq imujr i @XicesuDpifn ogfefeser vahlfooq.
Key points
There are no magic silver bullets with test data.
JSON data is great for quickly getting started with end-to-end tests but can become difficult to maintain as your test suites get larger.
Hard-coded data works well when your test suite is small, but it lacks variety in test data as your test suite grows.
Faker makes it easier to generate a variety of test data for object libraries.
Tests that need to get data stores into a certain state can be expensive because you need to insert data into the data store programmatically.
Where to go from here?
Wrangling your test data is another way that you’re able to move slow to go fast. In your case, going fast means more companions in homes and programmers with higher quality code. To learn more about Faker, check out the project page at https://github.com/DiUS/java-faker.
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