Project Management: You can review our task tracking and team distribution on the Sprint 1 Kanban Board (GitHub Projects).

During the previous Micro Sprints, our team explored different problems experienced by university students. After analyzing the interviews, situations, journey maps, brainstorming results, empathy maps, and personas, we selected the experience of finding and choosing food options at the university as the problem that we will continue developing.

The problem is not necessarily the absence of food options. Instead, the main difficulty is that students do not always have an easy way to identify which available food alternative best fits their needs at a particular moment.

University students often need to eat between classes, meetings, study sessions, and other academic activities. Because their available time may be limited, choosing where and what to eat becomes a decision influenced by several factors:

• Available time between activities.
• Price and personal budget.
• Distance from their current location.
• Restaurant opening hours.
• Waiting times and queues.
• Food availability.
• Dietary preferences or restrictions.
• Type and variety of food available.

Information about these factors is often fragmented or unavailable before the student reaches the restaurant. Students may therefore have to visit different places, ask other students, check social media, or simply choose an option they already know.

This situation can lead students to spend part of their limited break searching for food, encounter long queues, discover that a desired meal is unavailable, or repeatedly choose the same familiar alternatives because they do not have enough information to confidently explore other options.

University students have difficulty quickly identifying which food option best fits their available time, budget, location, dietary preferences, and current needs because relevant information about university food alternatives is fragmented or not easily accessible.

The main prospective users are university students who eat on or around campus during their academic day.

Different students may experience the problem differently. Some may prioritize speed because they have very little time between classes, while others may prioritize price, dietary preferences, proximity, or food variety.

• Knowing what food alternatives are currently available.
• Understanding prices before choosing a place.
• Finding options that fit within a limited amount of time.
• Knowing the location and distance of different food alternatives.
• Identifying food that matches dietary preferences or restrictions.
• Avoiding unnecessary waiting in long queues.
• Discovering alternatives beyond the restaurants they already know.
• Making the food selection process faster and easier.

How might we help university students make faster and better-informed decisions about where and what to eat during their time on campus?

Uniandes Food is a mobile application that centralizes the food options available on and around the university campus.

The application displays restaurants through an interactive map and provides relevant information that helps students compare alternatives before deciding where to eat.

Restaurant profiles can include information such as:

• Location.
• Distance from university buildings.
• Menus.
• Prices.
• Opening hours.
• Estimated waiting times.
• Food availability.
• Dietary alternatives.
• Accepted payment methods.
• Ingredient and allergen information.
• Student ratings and reviews.

Students can filter food options according to factors such as budget, location, dietary preferences, food type, and available time.

The application also includes a community component. Students can access peer reviews and, at participating restaurants, scan a QR code to directly access the review functionality and share their experience.

The scope of the application is deliberately limited to the university campus and its surroundings so that the map is not saturated with options that are irrelevant to the student's current situation.

The objective of Uniandes Food is to help students make faster and better-informed food decisions while also helping them discover alternatives beyond the restaurants they already know.

Uniandes Food operates under a multi-sided platform business model designed to remain 100% free and frictionless for university students while monetizing through local food businesses, commercial partners, and institutional insights:

1. Featured Listings and Promoted Placements (Freemium for Vendors):
   • While basic registration, menu display, and QR reviews are completely free for all campus food establishments, restaurants can purchase monthly featured placements on the map and top positions in search/filter results to increase student foot traffic during low-demand hours.
2. Promotional Banners and Student Deals:
   • Local restaurants can sponsor exclusive student discounts, combo promotions, or flash deals highlighted on the home screen feed, paying a small flat fee or cost-per-click (CPC) for targeted impressions within the campus geofence.
3. B2B Market Analytics and Demand Reports:
   • Aggregated, anonymized student demand data (peak dining hours, high-demand food categories, underserved dietary preferences, and price sensitivities) can be packaged into periodic market intelligence reports sold to local restaurants to help them optimize their inventory, staffing, and pricing strategies.
4. Sponsored Food Pop-Up Fairs:
   • The platform can partner with university student councils or local culinary brands to sponsor rotating food events or "pop-up stalls" on campus, charging a vendor registration commission for providing digital visibility, pre-orders, and promotional marketing.

Uniandes Food helps university students quickly discover and compare dining options that fit their current situation, including their available time, budget, location, dietary preferences, and food needs.

Instead of searching through different social media accounts, asking friends, walking between restaurants, or repeatedly choosing the same familiar place, students can access relevant information about nearby dining options through one centralized mobile application.

For restaurants around the university, the platform also creates value by increasing their visibility among students who may otherwise never discover them and by providing a space where their menus, prices, availability, and student experiences can be found.

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The following PAS hypotheses describe problems identified around the university dining experience, the alternatives students currently use, and the solutions proposed through Uniandes Food.

Problem	Alternative	Solution
Students with limited time between classes struggle to quickly identify an available dining option with a reasonable waiting time.	They usually choose the closest or most familiar restaurant, ask friends, or walk around checking alternatives.	Provide a mobile app that compares nearby options using distance, estimated waiting time, opening hours, and availability.
Students have difficulty finding food that fits their budget and dietary preferences because dining information is fragmented.	They check restaurants individually, ask friends, search social media, or repeatedly eat at familiar places.	Centralize menus, prices, and restaurant information in an app with filters for budget, dietary preferences, location, and food type.
Students often do not know which restaurants are suitable for a quick meal or takeaway when they have little time before their next class.	They choose a familiar place, ask friends which restaurant is faster, or arrive at a restaurant and decide based on how busy it looks.	Show whether each restaurant offers takeaway or dine-in service and provide an estimated total service time so students can choose an option that fits their available break.

Problem	Alternative	Solution
Students may avoid distant or unfamiliar food options because they are uncertain about the location, walking distance, prices, and surrounding area.	They avoid unfamiliar areas and continue choosing restaurants close to the buildings they normally use.	Show the restaurant location, walking distance, prices, and peer reviews so students have more information before deciding whether to visit the place.
Students do not always trust traditional advertising from restaurants they have never visited and often prefer recommendations from other students.	They wait for recommendations through friends, WhatsApp, Instagram, or word of mouth.	Include peer reviews and ratings and allow students to scan QR codes at participating restaurants to leave reviews based on their experience.

Problem	Alternative	Solution
Students may avoid trying new restaurants because they do not know their menus, prices, or portion sizes.	They ask friends, search for the restaurant on Instagram, or visit without knowing whether the restaurant fits their needs or budget.	Allow restaurant profiles to display menu photos, dish information, and prices so students can evaluate the option before walking there.
When a group of friends wants to eat together, finding a place that fits everyone's budget, dietary preferences, location, and available time can be difficult.	One person chooses, the group discusses restaurants informally through WhatsApp, or students split up and eat at different places.	Provide a group suggestion feature that considers the group's budget, dietary restrictions, available time, and location to recommend compatible restaurants.

Problem	Alternative	Solution
Students may arrive at a restaurant without knowing whether it accepts their preferred payment method.	They carry additional cash, ask when they arrive, or continue choosing restaurants whose payment methods they already know.	Display accepted payment methods for each restaurant, including cash, cards, and digital wallets.
Students with allergies or strict dietary needs may not know whether a meal contains ingredients they need to avoid.	They ask restaurant staff directly, avoid unfamiliar meals, or bring food from home.	Include ingredient and allergen information together with filters for dietary restrictions such as vegetarian, vegan, gluten-free, and lactose-free options.

Problem	Alternative	Solution
Students with limited time may abandon the application if nearby dining information takes too long to load.	They close the application, go directly to a familiar restaurant, or ask a friend for a recommendation.	Measure loading times and abandonment and cache relevant nearby restaurant information so options can be displayed quickly.
Students often arrive at the same restaurants during peak hours and encounter long queues or unavailable items.	They wait, walk to another restaurant, or sometimes skip the meal because they no longer have enough time.	Use reported waiting times and historical congestion information to recommend nearby alternatives with shorter expected waiting times.
Restaurants around campus do not know which food categories and dietary preferences students are actually looking for and failing to find, so they invest in dishes that already have plenty of competition while demand they could capture goes unserved.	They guess from what they see in their own line, copy what the neighbouring restaurants sell, or try new dishes and drop them when they do not sell, without ever knowing whether the demand existed somewhere else on campus.	Compare the searches and filters students apply against the options actually registered in the app, and sell the resulting demand-coverage gap to local restaurants as a periodic market report, so they can adjust their menu, inventory and pricing to the categories that are being requested and not offered.

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The personas represent complementary user profiles affected by the university food selection problem, as well as a stakeholder interested in the data generated by Uniandes Food.

The prospective personas capture different needs identified throughout the research process. Daniela Torres represents students with dietary restrictions and allergies who need reliable ingredient and allergen information. The Social Explorer represents students who value variety, social experiences, and discovering new restaurants but are affected by fragmented information and uncertainty about unfamiliar places. Nicolás Ramírez represents students with limited time between classes who prioritize proximity, waiting times, availability, and fast decision-making.

The Analytics Persona, Mateo Rincón, represents a stakeholder who uses application-generated data to understand student behavior, evaluate which features and restaurants generate the most engagement, and support product and business decisions.

• Profile & Demographics: An undergraduate university student (aged 18–23) on a limited student budget who views lunchtime as a vital social anchor in their daily academic routine. They enjoy organizing group meals and discovering new food experiences with friends.
• Goals & Motivations:
  • Wants Variety & Quality: Strives to discover hidden culinary gems around campus and avoid generic low-quality fast food, placing high trust in authentic student word-of-mouth rather than commercial advertising.
  • Wants Quick & Frictionless Dining: Needs food spots located conveniently close to class buildings to avoid wasting limited break times in long peak-hour queues.
• Pain Points & Frustrations:
  • Fear of crime and insecurity when walking to secluded or unfamiliar food spots.
  • Encountering unexpectedly high prices at hidden spots without prior budget warnings.
  • Wasting valuable study breaks searching across fragmented social media channels (Instagram/TikTok).
  • Frustration when group plans fall apart due to inaccurate menu or scheduling information.
• Environment & Tech Literacy: A tech-savvy digital native and early adopter who relies heavily on mobile apps, GPS mapping, digital wallets (Nequi/Daviplata), and social platforms to coordinate daily campus activities via smartphone and laptop.

Daniela Torres is a 21-year-old vegan university student with food allergies and a busy schedule who eats on campus every day.

Her main goal is to eat safely and confidently, without constantly worrying about hidden ingredients or cross-contamination. This is hard because most campus menus don't list ingredients or allergens, and staff often aren't sure how food was prepared — so she relies on a short list of trusted restaurants, double-checks every order, and reads labels obsessively.

Her key pain points are accidental allergen exposure, the high cost of specialized food, feeling excluded when friends pick places with no options for her, and wasting time questioning staff. What she wants most is an app to filter food by dietary restriction, plus staff trained on cross-contamination.

She's tech-comfortable and mainly uses her smartphone and laptop.

Mateo Rincón is a Product Manager with a Business Administration background, middle-class and a private university graduate who is active in tech and startup circles.

His main goal is to understand how students actually use the app so he can prove and grow its value. This is hard because restaurant information comes from scattered sources instead of one system, wait-time data is limited to self-reported QR reviews, and he can't easily tell whether long lines come from bad schedule info or unpredictable student behavior so he relies on whatever analytics and reviews he can gather to guide decisions.

His key pain points are not knowing which filters and restaurants students really use, seeing students view a restaurant on the map but never follow through with a visit or review, and struggling to measure real demand when he doesn't know why students skip certain places. What he wants most is solid analytics on student behavior and restaurant visits, both to bring on new restaurant partners with real data and to show investors the app genuinely saves students time.

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This empathy map represents an undergraduate student with a busy academic schedule who regularly needs to choose where to eat between university activities.

The Thinks section reflects the student's desire to eat something healthy and enjoyable, save time between classes, and have more variety close to their schedule. These thoughts are directly related to the difficulty of making food decisions when information about available options is fragmented.

The Sees section shows the external conditions that influence the decision: daily menus distributed across different restaurants, long queues during peak hours, incomplete information about prices, opening hours and food types, and some options being located far from the student's classes.

The Feels section captures the emotional consequences of these problems. The student feels frustrated when a preferred option is unavailable, stressed when there is not enough time to eat between classes, and satisfied when they find an option that is both convenient and enjoyable.

Finally, the Does section shows the behaviors students currently use to deal with the problem. They often choose the closest restaurant, eat at the same familiar places, search external applications or social media, and ask friends for recommendations.

Together, these observations reinforce the selected problem: students need a faster and more centralized way to compare university food options according to their time, location, budget, availability, and preferences.

This empathy map represents a university student who seeks culinary variety and enjoys social food experiences, but finds their choices heavily restricted by distance, student budget constraints, and concerns regarding campus security.

The Thinks section reveals the student's internal dilemma: they feel exhausted by eating the exact same meals every week, worry about wasting their tight budget on unverified hidden spots, and fear having their academic equipment (backpack/laptop) stolen if they walk to distant, secluded areas around campus.

The Sees section captures their daily visual environment: the same four repetitive food spots right outside their primary study building, friend recommendations and food stories on Instagram that lack clear price tags or exact locations, and distant food stalls that appear uncertain or lack visible menus.

The Feels section reflects emotional pain points: anxiety and hypervigilance regarding physical safety when venturing beyond familiar university zones, boredom and frustration with an "autopilot" eating routine, and immediate relief whenever they secure an affordable, tasty, and safe meal close to their lecture halls.

The Does section highlights current coping behaviors: sticking to known spots out of convenience, relying heavily on direct word-of-mouth from trusted friends, and actively refusing to walk to distant food areas unless accompanied or properly validated.

Together with the identified Pains (fear of insecurity, hidden costs, wasted time searching social media) and Gains (centralized peer-reviewed directory, route safety awareness, budget transparency), this empathy map underscores the core value proposition of Uniandes Food: democratizing access to diverse dining options while eliminating distance and safety frictions.

This empathy map describes a university student who has very little time between classes and has to decide where to eat in windows of about fifteen or twenty minutes. What matters most to this student is not price or variety, but speed and knowing what to expect.

In the Thinks section, the student is basically doing math against the clock. Fifteen minutes is not enough to gamble on a line, so the thoughts revolve around wanting to know in advance which places are empty. There is also a resigned idea here: if it comes down to eating well or getting to class on time, class wins. One of the thoughts sums it up well, saying that if there is a line, the break is already gone.

The Sees section covers what the student actually notices around campus. Lines form at exactly the wrong moment, during the class change. Classmates end up eating standing up or while walking to the next building. The fast-food stalls closest to the classrooms are always the most packed ones. The overall picture is people rushing between buildings trying to find something before the bell.

The Feels section is where the cost of all this shows up. There is anxiety when the clock is running and the line has not moved, frustration at ending up with something unhealthy just because it was the fast option, and relief on the days when an empty spot appears. What stands out is that the uncertainty about the wait bothers the student more than being hungry.

The Does section shows how the student copes right now. They go to the closest place without comparing anything, ask a classmate to hold their spot, and sometimes just skip the meal when nothing quick is available. The end result is eating whatever turns up first, which is usually not what they wanted.

The pains all come from the same place: no visibility. The student cannot know how long a line is before leaving the classroom, walks to places that turn out to be full, cannot compare options quickly, skips meals, and shows up late. The gains are the other side of that coin: knowing the wait beforehand, seeing nearby options ordered by how long they take, and deciding in a few seconds instead of walking around to check.

All of this points back to the same problem. Students need real-time information about wait times and availability nearby so they can make a decision that actually fits the time they have.

This empathy map represents a university student with strict dietary restrictions (such as being vegan or having food allergies) who faces the daily challenge of finding safe and suitable food options on campus.

The Thinks section reflects the student's constant worry about what food is actually made of, their wish for more plant-based protein options instead of just meat-based meals with the meat removed, and their need to find a trustworthy place to eat so they don't get sick before their next class.

The Sees section shows the external conditions the student notices around campus: staff who don't always seem sure how the food was prepared, the same two "safe" options every day without much variety, the risk of cross-contamination at salad bars or self-service areas, and menus that don't list all the ingredients or show allergen icons.

The Feels section captures the emotional toll this situation causes. The student feels frustrated by the lack of clear labeling on food sold on campus, stressed when staff seem annoyed by their detailed questions, and relieved and loyal when they find a place that truly understands their needs.

Finally, the Does section shows the behaviors the student uses to cope with the problem. They ask staff very specific questions about oils and broths before paying, stick rigidly to the one or two restaurants they already trust, and obsessively read ingredient labels on packaged snacks.

Students with dietary restrictions need clear, standardized information regarding allergens and ingredients and safe food options, as well as to avoid spending the time and energy required to determine where they can eat safely.

This empathy map represents an undergraduate student who enjoys trying new food options around the university and has enough time and budget to do so, but lacks a reliable and centralized way to discover them. The Thinks section shows that the student would explore new restaurants more often if they knew where to look. They do not want to waste time or money visiting a place that turns out to be mediocre, and they feel uncertain about choosing a new option without reliable information. The Sees section reflects the student’s environment: classmates repeatedly return to the same familiar restaurants out of habit, while new places open nearby without receiving much attention because students do not know whether they are worth visiting. The Feels section captures a mixture of curiosity and frustration. The student feels restless and slightly bored with the limited options they already know, and frustrated that discovering something new depends mostly on word-of-mouth. However, they feel satisfied and excited when they happen to find a genuinely good new restaurant. The Does section shows that the student actively searches for new places during free time between classes, talks with friends to obtain recommendations, but still ends up eating at the same restaurants every day when no clear alternative is available. The main pains are the lack of a centralized directory of food options, limited access to trustworthy reviews and ratings, and the time wasted trying to discover places through informal channels. The main gains would be one app to browse all nearby food options, access to honest student reviews, and the ability to discover restaurants they have not tried before.

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Which screens or features of Uniandes Food have the highest average loading time during use?

How long does the map take to load nearby dining options, and what percentage of students close the app before the results finish loading?

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Which information do students use the most when comparing dining options: price, distance, estimated waiting time, menu availability, or dietary preferences?

What percentage of students check a restaurant's photo menu before deciding to go, compared with those who go without checking it first?

What is the completion rate of users scanning a QR code at a physical location to leave a review versus users searching manually to leave a review?

How much time on average does a student spend setting up filters such as price, distance, and dietary preferences before finally selecting a restaurant profile?

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Which functionalities of the app are used least frequently when students are deciding where to eat?

How often is the "Suggest for a Group" feature used compared with individual searches when students are deciding where to eat?

How often do students use the allergen and dietary restriction filters compared to the other filters in the app, and which restrictions are selected the most?

How often do students filter dining options by 'proximity to a specific university building' (e.g., RGD, ML) compared to generic GPS radius filtering, and should building-based filtering be set as the default view?

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At which times of the day should the app suggest alternative restaurants to students so that demand is distributed and fewer students arrive at places with long lines or unavailable items?

Which food categories and dietary preferences are most demanded by students but least covered by the restaurants currently registered in the app?

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Which payment methods do the restaurants around campus actually accept, and how far is that from the methods students say they prefer to use?

How does dish photo loading latency (Type 1) affect the percentage of students who inspect photo menus before deciding (Type 2), and how does this correlate with overall profile view engagement across different food categories (Type 3)?

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(Click on the image to view in full resolution)

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Element	Description
User Action	A student opens Uniandes Food and selects filters for maximum walking distance, budget, dietary preferences, and the amount of time available before the next class.
System Response	The application processes the selected filters and displays nearby restaurants that match the student's criteria, showing relevant information such as distance, estimated waiting time, price range, opening status, and menu availability.
Execution Context	The student is on campus between classes, has limited time to eat, and needs to quickly choose a suitable restaurant without visiting multiple places or checking different information sources.

Element	Description
User Action	After eating at a participating restaurant, the student scans the restaurant's QR code through Uniandes Food to leave a review and rating.
System Response	The application identifies the restaurant linked to the QR code, opens its review form, allows the student to submit a rating and comments, and adds the review to the restaurant profile after successful submission.
Execution Context	The student has just finished eating at the restaurant and wants to share their experience with other university students. The QR code is physically available at the establishment and provides direct access to the correct restaurant profile.

Element	Description
User Action	A student currently in the RGD building selects "RGD" as their reference building, sets a maximum walking time filter of "under 5 minutes", and sets a price filter of "< $15,000 COP".
System Response	The application filters all registered restaurants, recalculates the walking distance relative to the selected building, and displays nearby options meeting the budget criteria along with their estimated walking duration and menu status.
Execution Context	The student has a short 20-minute break between lectures and needs to grab lunch quickly without walking long distances or exceeding their daily student budget.

Element	Description
User Action	A student discovers an unfamiliar food option on the map and taps its profile to inspect peer ratings, verified student reviews, and real dish photos uploaded by classmates.
System Response	The application displays the restaurant profile with verified student reviews, photo previews of meal portions, accepted payment methods, and an average student safety and price rating for the surrounding area.
Execution Context	The student wants to break their usual food routine and try something new, but is hesitant to walk blindly without social validation and certainty about prices and meal quality.

Element	Description
User Action	A student with dietary restrictions (e.g., vegan, gluten-free, or with food allergies) opens Uniandes Food, activates the "dietary restrictions" filter, and selects the specific restrictions and allergens they need to avoid.
System Response	The application filters out restaurants whose menus do not offer compatible options, highlights dishes that match the selected restrictions on each remaining restaurant profile, and flags menu items where allergen information is incomplete or unavailable.
Execution Context	The student is on campus, has a short time before their next class, and needs to avoid the stress and health risk of guessing whether a restaurant can safely accommodate their diet.

Element	Description
User Action	A student taps on a restaurant on the map to check if it is currently open.
System Response	The application shows the restaurant's opening hours and whether it is open or closed at that moment.
Execution Context	The student is deciding where to go and wants to avoid walking to a restaurant that turns out to be closed.

Element	Description
User Action	A student taps a restaurant on the map and indicates they are heading there before their next class.
System Response	The application detects that the restaurant is in a congestion peak for that hour, warns the student that the expected wait exceeds the time they have left, and proposes two nearby alternatives with shorter expected waits, showing for each one the walking time from the student's current building.
Execution Context	It is class-change hour, the student has around twenty minutes, and the place they picked is the one everyone goes to at that time.

Element	Description
User Action	A student finds a restaurant that fits what their group is looking for and uses the share option on the restaurant profile.
System Response	The application generates a link containing the restaurant name, its location on campus, price range, and current opening status, and hands it to the phone's native share sheet so the student can send it through WhatsApp, Instagram, or any other installed application.
Execution Context	The group is coordinating lunch through WhatsApp and one member wants the others to see the same information instead of describing the place in words.

Element	Description
User Action	A student finishes eating at a restaurant and scans the QR code displayed at the location to leave a review.
System Response	The application opens the review form for that specific restaurant, confirms the student’s visit through the QR code, and allows them to rate the restaurant, comment on their experience, and confirm information such as prices, menu items, and waiting time. Once submitted, the review is marked as verified and becomes visible on the restaurant profile.
Execution Context	The student is physically at the restaurant after lunch and wants to share reliable information with other students who are deciding whether to visit the place.

Element	Description
User Action	A student notices that a restaurant profile shows an outdated menu price or incorrect opening hour and selects the “Report outdated information” option.
System Response	The application asks the student to select the incorrect information and optionally provide an update or a photo of the current menu. It records the report and automatically sends an email notification to the restaurant administrator so they can review and update the restaurant profile.
Execution Context	The student is comparing food options before class and needs trustworthy information because their budget and available time are limited.

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Element	Description
Scenario Name	Unstable Internet Connection While Searching for Food
Quality Attributes	Availability, Resilience, Performance
App Status and Context	A student has little time between classes and is using Uniandes Food to check nearby restaurants, prices, and estimated waiting times. The student is connected through mobile data while moving between university buildings.
Changes in the Context	The internet connection becomes unstable or is temporarily lost while the student is using the restaurant map and filters.
System Reaction	The application should continue displaying the most recently cached restaurant information instead of leaving the user with an empty screen. It should clearly indicate that some information may not be updated, preserve the selected filters, and automatically attempt to refresh the information when the connection becomes available again.

Element	Description
Scenario Name	High User Traffic During Peak Lunch Hours
Quality Attributes	Scalability, Performance, Availability
App Status and Context	Uniandes Food is operating normally and students are simultaneously checking nearby restaurants, menus, waiting times, and availability around lunchtime.
Changes in the Context	A large number of students access the application at the same time during a peak lunch period, significantly increasing the number of requests to the system.
System Reaction	The application should continue responding without becoming unavailable or significantly delaying the main user flow. Restaurant lists, map results, and essential information should remain accessible, while less critical updates may be temporarily delayed or served from cached data if necessary.

Element	Description
Scenario Name	Degraded GPS Accuracy in Indoor Campus Environments
Quality Attributes	Usability, Fault Tolerance, Robustness
App Status and Context	A student is studying in a campus basement (e.g., Mario Laserna or RGD basement) where GPS satellite precision is weak or unavailable, attempting to locate nearby dining spots.
Changes in the Context	The mobile device's GPS signal is lost or returns highly inaccurate coordinates due to heavy concrete interference.
System Reaction	The application detects degraded GPS accuracy, prevents erratic map repositioning, and prompts the user with a quick manual selector to choose their current university building, ensuring nearby dining results remain instantly relevant.

Element	Description
Scenario Name	Concurrent Review and Photo Submissions at Peak Lunch Time
Quality Attributes	Scalability, Performance, Data Integrity
App Status and Context	Uniandes Food is operating during peak lunch hours (12:30 PM - 1:30 PM) when hundreds of students are simultaneously finishing meals and scanning QR codes to submit reviews and photos.
Changes in the Context	A sudden spike of concurrent write requests (text reviews, star ratings, and photo uploads) hits the backend server within a short timeframe.
System Reaction	The application provides immediate optimistic feedback on the student's screen, queues image compression and uploads asynchronously in the background, and processes submissions reliably without app freezes, duplicate entries, or HTTP 500 server crashes.

Element	Description
Scenario Name	Reduced Functionality Under Low Battery Conditions
Quality Attributes	Performance, Resilience
App Status and Context	A student is using Uniandes Food near the end of the day, and their phone battery is critically low after a full day of classes.
Changes in the Context	The phone enters low-power/battery-saving mode, restricting background processes and reducing available resources for the app.
System Reaction	The application detects the low battery state, reduces non-essential background activity (such as live location updates and image pre-loading), keeps core features (search, filters, restaurant info) responsive, and avoids crashing or freezing due to resource constraints.

Element	Description
Scenario Name	Screen Reader Compatibility While Browsing Restaurant Options
Quality Attributes	Accessibility, Usability
App Status and Context	A student with a visual impairment is using a screen reader on their phone to navigate Uniandes Food and find a restaurant that fits their dietary needs and available time.
Changes in the Context	The student moves through filters, restaurant cards, and menu details using voice navigation instead of visual interaction.
System Reaction	The application adds clear labels and descriptions to all buttons, filters, and icons so the screen reader can read them, keeps the reading order simple and logical, and gives sound feedback when the student does something, like applying a filter or placing an order.

Element	Description
Scenario Name	Damaged, Unreadable or Tampered QR Code
Quality Attributes	Fault Tolerance, Data Integrity, Usability
App Status and Context	A student finishes eating at a participating restaurant and opens the scanner to leave a review through the QR code displayed at the establishment.
Changes in the Context	The printed code is scratched, poorly lit, partially covered, or has been replaced by a sticker pointing to a different restaurant.
System Reaction	The application should fail explicitly instead of opening a wrong review form. If the code cannot be decoded after a few seconds it offers a manual search restricted to restaurants near the student's current position; if the code decodes but does not correspond to a registered establishment, or the student's location does not match the restaurant it points to, the review is rejected and the incident is logged for the operations team.

Element	Description
Scenario Name	Protection of Student Location and Behavioral Data
Quality Attributes	Security, Privacy, Compliance
App Status and Context	The application collects location and interaction events to recommend nearby restaurants and to build the aggregated demand reports sold to restaurant partners.
Changes in the Context	The student sends the app to the background or denies the location permission, and a restaurant partner requests the demand data of their establishment.
System Reaction	The application stops requesting location as soon as it goes to the background, keeps working with a manually selected building when the permission is denied, and exposes data to partners only in aggregated and anonymized form. Any group small enough to identify a single student is suppressed from the report.

Element	Description
Scenario Name	Detection and Handling of Outdated Restaurant Information
Quality Attributes	Information Quality, Modifiability
App Status and Context	Uniandes Food displays restaurant profiles containing menus, prices, opening hours, dietary information, and estimated waiting times. Students rely on this information to choose where to eat within a limited break.
Changes in the Context	A restaurant changes its prices, menu, or opening hours, but the information stored in the application has not been confirmed or updated for a defined period of time.
System Reaction	The application should always display current and verified restaurant information. When a restaurant updates its prices, menu, opening hours, dietary information, or availability, the system must update the restaurant profile before showing that information to students in the map, search results, filters, and restaurant details.

Element	Description
Scenario Name	Adding New Dining Options Without Releasing a New App Version
Quality Attributes	Modifiability, Maintainability, Deployability
App Status and Context	Uniandes Food is operating normally and students use the map to discover food options on and near campus. A new restaurant opens, or an existing restaurant needs to update its profile information.
Changes in the Context	An administrator needs to add the new restaurant—including its location, opening hours, price range, menu, dietary options, and QR code—or modify an existing profile.
System Reaction	The system should allow authorized administrators to create or update the restaurant profile through a management interface. Once validated, the change should become available in the app map, search results, and filters without requiring students to download a new version of the application.

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https://youtu.be/EOIlo6P_7kw