Aspire Systems and an anonymised fashion and apparel retailer: Salesforce Commerce Cloud test automation case analysed (case 1)
Verdict: what is checkable
The evidence grade (this site's label for how a case is sourced: primary with client or press confirmation, secondary when vendor-hosted only.) is secondary: the only source is the vendor's own page; of 9 metrics, 0 are verifiable yes, 0 partial and 9 no. [ev-1123]
Client profile as published
- Client
a leading American plus-size apparel and home goods company based in New York City
(the vendor's description; the vendor does not name the client)- Segment
- Fashion and apparel
- Region
- United States
- Channels
- ecommerce
- Size indicators
For more than 100 years
;a huge customer base in the country
The client description and size indicators are quoted from the source; segment, region and channels are this site's classification. [ev-1123]
Systems in scope
- Ecommerce platform (Salesforce Commerce Cloud): named in The Result section (DCqaf components reused in Salesforce Commerce Cloud); BM site named in Aspire's Solution
Where the source names each system is quoted or summarised from the case page. [ev-1123]
Problem as stated
The apparel retailer sells online and by catalog and needed its site to run across 13 browsers and several device types. Its linear test automation did not scale, daily data repetition caused repeated data loss, and continuous integration with its build server and issue tracker was hard to set up. It wanted a framework that could grow with a complex application while keeping infrastructure spending low. [ev-1123]
Linear automation testing was not scalable(quoted from the source)
Approach
- Testing types
- automation, functional and mobile (main scope: automation)
- Team shape
- not stated
- Duration
- not stated
- Engagement model
- not stated
- Tooling named in the source
- DCqaf, Auto POM, AI-led self-healing scripts, ML-led dashboard, BM automation framework, Jira, TestRail, Sauce Labs, Selenium, Jenkins, Java
Metrics claimed
| Metric | Value | Unit | Claimed by | Verifiable | Evidence |
|---|---|---|---|---|---|
| test duration | 50 person days to 18 person days | person days | vendor | no | ev-1124 |
| authoring time and effort reduced with Auto POM | 80 | % | vendor | no | ev-1125 |
| resources required | 8 to 4 | resources | vendor | no | ev-1126 |
| bugs found early during scripting | 25 | % | vendor | no | ev-1127 |
| maintenance cost reduction | 35 | % | vendor | no | ev-1128 |
| projected cost savings for one year | $150,000 | USD | vendor | no | ev-1129 |
| automation effort saved with DCqaf | 30 | % | vendor | no | ev-1130 |
| browser combinations in regression run | 13 | browser combinations | vendor | no | ev-1131 |
| platforms covered by a single script | 3 | platforms | vendor | no | ev-1132 |
What the case does not say
- Measurement method.
- Duration over which the metric held.
- Date of the work.
- What was out of scope.
- Whether the client is still a client.
Reading notes for a buyer
This case speaks to the scenario “Ecommerce platform migration” and feeds dimension D4 (Test automation evidence) of the methodology. A question to put to Aspire Systems:
Is the reported dollar saving a projection or a measured result, and which cost base was it calculated from? [ev-1123]
Source and verification
- Source
- Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer
- Published
- not dated by the vendor
- Verified
- 2026-10-30
- Evidence grade
- secondary
- Corroborating sources
- none found
| Id | Claim | Source | Class | Grade | Published | Verified |
|---|---|---|---|---|---|---|
| ev-1123 | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1124 | Test duration was brought down from 50 person days to 18 person days | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1125 | Time and effort reduction by 80% with accelerated authoring using Auto POM. | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1126 | The number of resources required for the process halved, from 8 to 4 | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1127 | 25% of the bugs were identified at an early stage as scripting | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1128 | Maintenance costs were cut by 35% with the implementation of AI-powered self-healing scripts and machine-learning algorithms. | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1129 | Projected cost savings of $150,000 for 1 year with 24 sprint cycles for 1 site | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1130 | Using DCqaf automation framework, 30% of the automation efforts were saved | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1131 | Entire automation regression suite was executed for all brands and 13 browser combinations before code freeze stage without any bugs | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 | |
| ev-1132 | A single test script worked on all 3 platforms - desktop, mobile & tablet | Future-proof test automation solution using AI-led DCqaf framework for American plus-size fashion retailer | vendor-case-study | secondary | 2026-10-30 |
Vendor profile: Aspire Systems. System facet: Ecommerce platform testing vendors.
Positions on this page are computed from the methodology published at /methodology/ and from the evidence records linked on this page. Case study figures are the vendor's or the client's claims as published at the linked source; the "verifiable" column states what this site could check. The site does not recommend vendors, does not accept paid placement and does not publish rates. Last computed: 2026-11-03.
Published by the editor of retailqavendors.com. Last reviewed .