Appbackr Might Have A Better Way To Determine Good Apps From Bad


Mobile users need better ways to figure out which apps are the best. In fact, with people downloading fewer apps, an arugment can definitely be made that the need for some kind of semi-subjective way to separate the wheat from the chaff  in app stores of all kinds is currently greater than ever.

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That’s where Palo Alto, California’s Appbackr comes in. The company uses machine learning techniques to assign a quality score to apps by testing things like the design of their code, how they affect a device’s memory and power usage and how users interact with them. Appbackr also has a way to evaluate an app’s “campaign promises,” comparing how the developer describes the app with how it actually operates and performs.

The result is a numeric score on a ten-point scale, with the idea being that it can be the gold standard for subjective app quality. Appbackr also offers app developers a platform to publicize their scores, increasing their chances of getting high-scoring apps into places like the Windows Phone Store, Amazon Appstore and manufacturer or carrier stores from the likes of Samsung and T-Mobile.

Naturally, it would be nice if the “Big Two” of the iOS App Store and Google Play would come around to a concept like this, but neither Apple or Google has much incentive for changing what they’re doing in their own ecosystems at the moment. More information on Appbackr and how its system works is available at

(via VentureBeat)