A method, comprising: executing, on a server of a cloud testing system, a video game for play by a player, said executing the video game producing a video output; receiving, from a client device of the player, a sequence of control inputs for interacting with the video game, a portion of the sequence of control inputs causing a bug to appear during said interacting with the video game; recording the portion of sequence of control inputs that caused the bug along with the video output and game state data produced by the video game in a player-generated snapshot file; processing the player-generated snapshot file using a machine learning module, the machine learning module extracting features from the sequence of control inputs, the video output, and the game state for classification into a bug detection model; and identifying, using the bug detection model, a set of test control inputs that are likely to reproduce the bug by the video game; the method further comprising: executing a plurality of automated video game sessions while respectively inputting the set of test control inputs for reproducing the bug, said executing the plurality of automated video game sessions each producing respective game state data and respective video components, wherein the respective test control inputs, game state data, and video components are recorded in respective machine-generated snapshot files in a snapshot database of the cloud testing system; performing, using the machine learning module, classification of the respective snapshot files based on whether the bug was reproduced or not and updating the bug detection model based on the classification; and extracting bug-implementation conditions from the bug detection model, the bug-implementation conditions being usable to identify additional control inputs causative of the bug; wherein the bug-implementation conditions include one or more rules associated with the bug, the one or more rules being usable to identify a category of sequences of control inputs that are causative of the bug, the category of sequences of control inputs including the sequence of control inputs and additional sequences of control inputs. executing, on a server of a cloud testing system, a video game for play by a player, said executing the video game producing a video output; receiving, from a client device of the player, a sequence of control inputs for interacting with the video game, a portion of the sequence of control inputs causing a bug to appear during said interacting with the video game; recording the portion of sequence of control inputs that caused the bug along with the video output and game state data produced by the video game in a player-generated snapshot file; processing the player-generated snapshot file using a machine learning module, the machine learning module extracting features from the sequence of control inputs, the video output, and the game state for classification into a bug detection model; and identifying, using the bug detection model, a set of test control inputs that are likely to reproduce the bug by the video game; the method further comprising: executing a plurality of automated video game sessions while respectively inputting the set of test control inputs for reproducing the bug, said executing the plurality of automated video game sessions each producing respective game state data and respective video components, wherein the respective test control inputs, game state data, and video components are recorded in respective machine-generated snapshot files in a snapshot database of the cloud testing system; performing, using the machine learning module, classification of the respective snapshot files based on whether the bug was reproduced or not and updating the bug detection model based on the classification; and extracting bug-implementation conditions from the bug detection model, the bug-implementation conditions being usable to identify additional control inputs causative of the bug; wherein the bug-implementation conditions include one or more rules associated with the bug, the one or more rules being usable to identify a category of sequences of control inputs that are causative of the bug, the category of sequences of control inputs including the sequence of control inputs and additional sequences of control inputs. executing a plurality of automated video game sessions while respectively inputting the set of test control inputs for reproducing the bug, said executing the plurality of automated video game sessions each producing respective game state data and respective video components, wherein the respective test control inputs, game state data, and video components are recorded in respective machine-generated snapshot files in a snapshot database of the cloud testing system; performing, using the machine learning module, classification of the respective snapshot files based on whether the bug was reproduced or not and updating the bug detection model based on the classification; and extracting bug-implementation conditions from the bug detection model, the bug-implementation conditions being usable to identify additional control inputs causative of the bug; wherein the bug-implementation conditions include one or more rules associated with the bug, the one or more rules being usable to identify a category of sequences of control inputs that are causative of the bug, the category of sequences of control inputs including the sequence of control inputs and additional sequences of control inputs.