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‎Data/DynamicData/amazon_book_search_result_dynamic.yml

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@@ -36,7 +36,192 @@ amazon_product_description: "Product description\nReview\n\"[Hosanagar's] 'predi
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\ appeared in Wired, Forbes, and the Harvard Business Review, and his past consulting\
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\ and executive education clients include Google, American Express, Citigroup and\
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\ SunTrust Bank. Hosanagar earned his PhD in Management Science and Information\
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\ Systems from Carnegie Mellon University.\nSee all Product description"
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\ Systems from Carnegie Mellon University.\nExcerpt. \xA9 Reprinted by permission.\
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\ All rights reserved.\n1.\n Free Will in an Algorithmic World\n\nIf you did consider\
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\ your choices, you'd be confronted with a truth you cannot comprehend: that no\
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\ choice you ever made has been your own. You've always been a prisoner. What if\
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\ I told you I'm here to set you free?\n\"The Man in Black\", Westworld, season\
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\ 1, episode 4\nTai, a senior at the University of Pennsylvania, wakes up at the\
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\ perfect time every morning-well rested, but not late for classes or appointments.\
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\ Today that meant rising at 7:18 a.m. He did not set his alarm for that time. Rather,\
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\ it was chosen for him. His phone's sleep-tracker app had been following his sleep\
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\ patterns over the past few months, monitoring his REM cycles and periods of lighter\
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\ rest. Using this information, it set a smart alarm that wakes him during a light\
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\ stage of sleep, while also trying to maintain some level of consistency over time.\
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\ The theory is that this schedule will prime Tai for greater energy and concentration\
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\ throughout the day.\nTai needs to be sharp. He's at a turning point in his life,\
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\ about to step away from the relatively safe world of college-of information-gathering,\
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\ homework, and exams-into the \"real\" world of practical problem solving: finding\
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\ a job, choosing a place to live, and negotiating the wonderful but complicated\
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\ details of a romantic relationship that's getting more serious by the day.\nAlthough\
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\ Tai is open to advice from his professors, his friends, and his family, he also\
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\ wants to go his own way. He considers himself to be an independent thinker, capable\
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\ of weighing lots of different options and then choosing the right one himself.\
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\ He needs a good mind for that-and a good night's sleep.\nTai rolls over in bed\
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\ and with one eye open grabs his phone and checks his notifications: fourteen likes\
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\ on his latest Insta, seven Facebook notifications, and three comments on his new\
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\ Facebook profile picture. Not bad for a Monday night. He scrolls down his Facebook\
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\ feed. An article shared by his friend Harry grabs his attention with its headline,\
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\ \"The Wealth of New Choices With Robot Vacuum Cleaners.\" He clicks and, liking\
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\ what he reads about the Eufy RoboVac cleaner, forwards the article to his girlfriend,\
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\ Kate.\nThere's an email from his mom, too, with a link to a New York Times article,\
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\ \"What I Wish I'd Known Before Moving in Together.\" Tai groans. Mention even\
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\ a possibility to his mother, and she sets it in stone. The picture accompanying\
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\ the article shows an attractive couple in their thirties sitting on an unblemished\
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\ white staircase, smiling into each other's eyes. He types, \"Haha thanks. that\
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\ middle-aged couple looks happy, see. How did you find this?\" Calling them middle-aged\
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\ will definitely get on his mom's nerves. But there's no time for more needling:\
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\ it's already 7:28 a.m.\nTai rolls out of bed and, walking across his dusty carpet,\
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\ opens his dresser, pulling out a pair of stretch washed chinos from Bonobos (he\
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\ follows the online clothing retailer on Instagram), blue-and-gray argyle socks\
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\ (top rated on Amazon), and a dress shirt and tie. He has a job interview today.\n\
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As he sits down for breakfast, Tai thinks of the fortuitous circumstances that led\
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\ to the interview. He had found the job posting through his friend Samantha, whom\
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\ LinkedIn's algorithms had reminded him to congratulate on her six-month work anniversary.\
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\ Their conversation had been a little awkward, as Tai and Samantha had matched\
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\ on Tinder a few years earlier. She was an artsy girl with a bubbling self-confidence;\
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\ lots to like about her, but neither of them felt any sparks. And although they\
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\ became friends, it had been hard for Tai to keep up with her since she graduated,\
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\ especially since Kate wasn't Samantha's biggest fan.\nTai's friendship with Samantha\
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\ is hardly the only thing that's been getting on Kate's nerves lately. Their discussion\
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\ about possibly moving in together seems to be stressing her out. Over the weekend,\
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\ Tai had sent Kate a Huffington Post recommended article: \"15 Things Couples Should\
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\ Do Before Moving in Together,\" which she read with great interest-especially\
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\ point number 15, \"Have an exit strategy.\" Tai had suggested that if they did\
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\ split up, it would make sense for her to be the one to move out-after all, he\
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\ had found the new apartment for the two of them. But it was only a contingency\
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\ plan. Her angry texts on the subject were still awaiting his reply.\nAfter dressing,\
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\ Tai checks his phone again to see if there are more texts. Nothing new from Kate,\
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\ but there is a reply from his mom about the Times article: \"Oh, I was looking\
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\ for housewarming gifts for you and Kate and it popped up on Google. Why don't\
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\ you send it to her, sweetie? And good luck on your interview this morning!\"\n\
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Tai can hear Chance the Rapper, chosen for him by Spotify Discover, rapping on the\
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\ other side of his bedroom wall, which is now glowing with the light of the rising\
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\ sun from the east window. It's time to head out for the interview. He looks for\
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\ an Uber to take him to campus. The price is $11.23, which feels a bit steep; yesterday\
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\ it had been $9.34 for the same route. He closes the app and relaunches it. The\
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\ price is now $10.82. It's not clear to Tai why it changed, but he confirms the\
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\ booking this time and waits at his door for the Toyota Corolla to pull up.\nAs\
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\ he exchanges pleasantries with the driver, Tai opens a notebook to work on his\
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\ case interviews, the part of business school job applications where students are\
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\ asked to think through a challenging business scenario and present a solution.\
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\ The case prep document shared by another student includes the question: What is\
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\ Root Cause Analysis? Tai jots down some notes, applies that technique to analyze\
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\ his day today, and produces a diagram:\nIt all seems kind of random at one level.\
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\ But he can't help but wonder about the degree to which the algorithms employed\
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\ by Facebook, Google, Tinder, and Amazon have a role to play in his present circumstances.\
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\ Will he have some cooked-up equation from a programmer to thank for his next job?\
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\ And is this job really the best next step for his life and career, or just the\
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\ accidental result of inconsequential past decisions-clicks of a mouse and swipes\
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\ on a screen? Tai likes to think of himself as being in the driver's seat. But\
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\ this Uber ride suggests he's not-both figuratively and literally.\nOr maybe he's\
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\ just overthinking things-the aftereffect of an in-class discussion I led on personalization\
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\ algorithms just a few days earlier. He sends me an email: \"Have something interesting\
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\ to show you. Do you have ten minutes after class?\"\nTai sighs and shuts his notebook.\
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\ Maybe all he and Kate need is to get away for a bit to reconsider this moving-in\
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\ idea. He pulls out his phone and opens Expedia's app. It might have some good\
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\ hotel recommendations.\nSince 2004 I've been teaching a class at Wharton called\
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\ \"Enabling Technologies.\" In hindsight I should have named it \"What's Going\
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\ On in Tech,\" because that's a more accurate and descriptive name. The course\
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\ examines the technologies that are shaping entire industries as well as the daily\
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\ lives of countless individuals-including students like Tai. In 2004, we covered\
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\ broadband technologies, online shopping, and Voice over IP (the ability to make\
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\ voice calls over the internet using services such as Skype). Today, those topics\
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\ seem almost mundane, as we now discuss the Internet of Things, virtual reality,\
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\ and space tech. One topic that has remained a constant in the course through the\
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\ years is algorithmic decision making. Although we once discussed Amazon's product\
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\ recommendations, we now consider algorithms in such applications as driverless\
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\ cars and robo-advisers. An additional and subtler change is that the sort of question\
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\ that Tai asked-to what extent are we in control of our own actions?-is coming\
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\ up in the class more and more often.\nAll of us realize how much of our lives\
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\ are shaped by the decisions we make online, whether through searches on Google,\
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\ connecting with friends on Facebook, or shopping on Amazon. Many of us are aware\
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\ that the companies running these sites are guiding our choices, often by customizing\
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\ our experience on their websites and apps. Personalization algorithms help us\
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\ choose the optimal products to buy on Amazon, the best movies to watch on Netflix,\
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\ the ideal person to date through Tinder and Match.com, the most useful contacts\
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\ on LinkedIn, and the most compelling posts and articles to read on Facebook. But\
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\ in our imagining, we generally nod politely at these recommendations and make\
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\ our own choices. After all, we are in charge here.\nAnd yet consider these facts:\
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\ 80 percent of viewing hours streamed on Netflix originate from automated recommendations.\
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\ By some estimates nearly 35 percent of sales at Amazon originate from automated\
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\ recommendations. And the vast majority of matches on dating apps such as Tinder\
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\ and OkCupid are initiated by algorithms. Given these numbers, many of us clearly\
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\ do not have quite the freedom of choice that we believe we do.\nOne reason is\
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\ that products are often designed in ways that make us act impulsively and against\
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\ our better judgment. For example, suppose you have a big meeting at work tomorrow.\
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\ Ideally, you want to spend some time preparing for it in the evening and then\
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\ get a good night's rest. But before you can do either, a notification pops up\
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\ on your phone indicating that a friend tagged you on Facebook. This will take\
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\ a minute, you tell yourself as you click on it. But after logging in you discover\
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\ a long feed of posts by friends. A few clicks later you find yourself watching\
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\ a YouTube video that one of them shared. As soon as the video ends, YouTube suggests\
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\ other related and interesting videos. Before you know it, it's 1:00 a.m., and\
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\ it's clear that you will need an all-nighter to get ready for the following morning's\
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\ meeting. This has happened to most of us. The reason this behavior is so common,\
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\ as some product designers have noted, is that popular design approaches-such as\
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\ the use of notifications and gamification to increase user engagement-exploit\
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\ and amplify human vulnerabilities, such as our need for social approval, or our\
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\ inability to resist immediate gratification even when we recognize that it comes\
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\ with long-term costs. While we might feel as if we are making our own choices,\
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\ we're often nudged or even tricked into making them.\nAnother reason that we aren't\
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\ truly in control of our choices is that when we search for a hotel on Expedia,\
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\ browse online dating profiles, or shop for a book, we're seeing only a small fraction\
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\ of all the potentially relevant information available. Although we experience\
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\ a clear sense of free will by making the final decision regarding what we see,\
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\ read, or buy, the fact is that 99 percent of all possible alternatives were excluded.\n\
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You probably don't mind saving all the time you might have wasted in sifting through\
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\ inferior options to arrive at a final choice. But algorithms do not simply help\
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\ us find products or information more quickly that we might have found eventually\
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\ without their assistance. In truth, they exert a significant influence on precisely\
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\ what and how much we consume.\nConsider the role of search algorithms. Given millions\
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\ of possible search results, thousands of which are likely to be highly relevant\
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\ to a particular query, Google's algorithms determine which ones are featured at\
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\ the very top of the results page. This ranking exerts a powerful influence on\
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\ our responses. About 33 percent of clicks go to the number-one result in Google\
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\ searches; fewer than 10 percent go to links outside of the top ten results.\n\
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Automated recommendations are also a major driver of choice online. More than any\
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\ individual or organization -including Oprah, the National Book Awards, or The\
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\ New York Times- Amazon's recommendation algorithms have the biggest influence\
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\ on which books people are reading. Automated recommendations drive purchase decisions\
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\ across a wide variety of product categories, from kitchenware and perfumes to\
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\ electronics and artwork. Beyond retailers such as Amazon and Walmart.com, online\
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\ media companies such as Netflix, Spotify, Apple's iTunes, and Google's YouTube\
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\ all employ algorithmic recommendations to gently nudge us in specific directions.\n\
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The impact of algorithms are also experienced on social media websites, where we\
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\ are likely to believe that our friends are the chief drivers of the content we\
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\ see. In reality, algorithms play an equally important role. Every time a user\
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\ opens Facebook's app or website, there are on average about 1,500 potential stories\
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\ or posts that Facebook can show. Its algorithms determine which ones you should\
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\ read first, which can be read later, and even which you don't need to read at\
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\ all. The algorithms consider a number of factors to determine the posts they show\
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\ us-how often we interact with the friend who posted; the number of likes, comments,\
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\ and shares that the post received in aggregate and from our friends; how recently\
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\ the post appeared; whether any users tried to hide it; and so on. Instagram and\
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\ Twitter have also recently adopted algorithmic feeds. Given how social networks\
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\ have become the gateway for online news and media discovery for so many of us,\
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\ news-feed algorithms are crucial to determining the reportage we read and the\
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\ opinions we form about the world around us.\nPerhaps inevitably, algorithms are\
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\ not just selecting the media we see on social networks but are also silently determining\
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\ the network itself-that is, whom we allow into our personal and professional lives.\
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\ LinkedIn's algorithms will magically remind you of the people you met last week\
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\ or emailed yesterday so they can be added to your professional network. Interested\
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\ in reconnecting with childhood friends? Facebook's algorithms will recommend whom\
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\ to add as friends on Facebook. And why stop at friends? Algorithms built by companies\
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\ such as Tinder, Match.com, and eHarmony will even determine whom you date or marry.\
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\ Algorithms are the primary drivers of matches on online dating apps and websites,\
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\ which, by some estimates, are used by as much as 40 percent of the singles population\
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\ in the United States.\nOpinions are one force in determining the actions we take\
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\ as individuals; our feelings are another. Consider the case of Match.com, launched\
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\ in 1995 and now the country's most popular dating website.\nGary Kremen, the man\
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\ who conceived of Match.com, was inspired in part by newspaper classified ads.\
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\ If you're old enough, you might remember that placing a personal ad usually involved\
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\ giving a few details about yourself and a few about the person you were looking\
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\ to meet: \"Single male, 35, avid reader, seeks single female, 20-30, fit and fun.\"\
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\ The job of finding a match was left to the reader of the ad. Match.com's earliest\
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\ algorithms sought to replicate this model, but also to step in as the actual matchmaker,\
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\ noticing the single male whom the busy single woman might have missed and putting\
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\ the two in touch."
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amazon_product_detail_description: 'A Wharton professor and tech entrepreneur examines
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how algorithms and artificial intelligence are starting to run every aspect of our
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lives, and how we can shape the way they impact us
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2 customer reviews
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Amazon Bestsellers Rank: #1,77,190 in Books (See Top 100 in Books)
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Amazon Bestsellers Rank: #1,78,801 in Books (See Top 100 in Books)
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#2289 in Industries & Business Sectors (Books)
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#2333 in Industries & Business Sectors (Books)
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#150 in Algorithms
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#154 in Algorithms
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#189 in Programming Algorithms
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#190 in Programming Algorithms
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Would you like to tell us about a lower price?
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@@ -105,7 +290,8 @@ amazon_product_offers: "Save Extra with 4 offers\nCashback: Flat Rs.50 back on m
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\ Pay UPI Here's how\nNo Cost EMI: Avail No Cost EMI on select cards for orders\
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\ above \u20B93000 Here's how\nBank Offer: 10% Instant Discount up to Rs. 1500 on\
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\ minimum purchase of Rs. 5,000 with SBI Credit cards and Credit Card EMIs Here's\
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\ how\nPartner Offers (1): Get GST invoice and save up to 28% on business purchases.\
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\ how\nPartner Offers (2): Avail EMI on Debit Cards. Get credit up to \u20B91,00,000.Check\
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\ eligibility here! Here's how\nGet GST invoice and save up to 28% on business purchases.\
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\ Sign up for free Here's how"
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amazon_product_title: 'A Human''s Guide to Machine Intelligence: How Algorithms Are
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Shaping Our Lives and How We Can Stay in Control'

‎Tests/amazon.py

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@@ -103,15 +103,21 @@ def test_amazon_book_search_001():
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ui_dynamic_data["amazon_product_detail_description"] = AmazonProductPage. \
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amazon_product_detail_description.texts_as_string()
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driver.switch_to_default_content()
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# Intentionally adding following details which are vulnerable to change dynamically.
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# While running `snap=1 pytest` first time it will fail and next time it should pass.
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# Amazon.in has iframe components in it and few sensitive elements.
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# To get the standard result kindly refrain from switching tabs or run with `headless=1 pytest` mode.
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# This will ensure that that test browser is not interrupted in middle.
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AmazonProductPage.amazon_product_description.scroll_to_locator()
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ui_dynamic_data["amazon_product_description"] = AmazonProductPage.amazon_product_description.texts_as_string()
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# Intentionally adding following two details which are vulnerable to change dynamically.
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# While running `snap=1 pytest` first time it will fail and next time it should pass
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AmazonProductPage.amazon_product_offers.scroll_to_locator()
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ui_dynamic_data["amazon_product_offers"] = AmazonProductPage.amazon_product_offers.texts_as_string()
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AmazonProductPage.amazon_product_details.scroll_to_locator()
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ui_dynamic_data["amazon_product_details"] = AmazonProductPage.amazon_product_details.texts_as_string()
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AmazonProductPage.amazon_product_description.scroll_to_locator()
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ui_dynamic_data["amazon_product_description"] = AmazonProductPage.amazon_product_description.texts_as_string()
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with allure.step("Compare the dynamic value from UI with the stored file"):
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# Now compare the UI value with the stored value. This comparison is taken care in `selpy` module.
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# assertion will be made in the selpy module itself and you could see those in the allure reports.
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dynamic_variable.compare(ui_dynamic_data)

‎requirements.txt

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@@ -11,4 +11,4 @@ ipdb == 0.12.3
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pyyaml == 5.3
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flake8 == 3.7.9
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webdriver-manager == 2.3.0
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selpy==0.0.7
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selpy==0.0.8

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