Abstract: A heuristics-based concurrent edit detector (“ConE”) can notify collaborators about potential conflicts that may be caused by edits made by other collaborators. ConE may compare concurrent edits submitted by collaborators, calculate the extent of overlap between two sets of edits, apply one or more filters to balance recall versus precision, and decide whether to alert the collaborators about candidate potential conflicts. ConE may be light-weight and easily scalable to work in a very large environment with numerous collaborators.

Abstract: A natural language code search service provides idioms or frequently-occurring code patterns for a code fragment based on similar type usage and method/API invocation usage. The search service uses a data mining technique that mines code snippets found from various websites and code snippets generated from a neural model to detect idioms in the code snippets that were previously unknown and which can be reused. A search is initiated through a natural language query within a code development tool or application thereby avoiding the need to switch out of the current application to perform the search.

Abstract: A data mining technique is used to find large frequently-occurring source code patterns from methods/APIs that can be used in code development. Simplified trees that represent the syntactic structure and type and method usage of a source code fragment, such as a method, are mined to find closed and maximal frequent subtrees which represent the largest frequently-occurring source code patterns or idioms associated with a particular type and method usage. These idioms are then used in an idiom web service and/or a code completion system to assist users in the development of source code programs.

Abstract: A heuristics-based concurrent edit detector (“ConE”) can notify collaborators about potential conflicts that may be caused by edits made by other collaborators. ConE may compare concurrent edits submitted by collaborators, calculate the extent of overlap between two sets of edits, apply one or more filters to balance recall versus precision, and decide whether to alert the collaborators about candidate potential conflicts. ConE may be light-weight and easily scalable to work in a very large environment with numerous collaborators.

Abstract: Described herein is a system and method for detecting correlated changes (e.g., between code files and configuration files). For a plurality of code files and a plurality of configuration files, a correlated change model is trained to identify correlated changes across the code files and the configuration files using a machine learning algorithm that discovers change rules using a support parameter, and, a confidence parameter, and, a refinement algorithm that refines the discovered change rules. The correlated change model comprising the change rules is stored. The correlated change model can be used to identify potential issue(s) regarding a particular file (e.g., changed code or configuration file(s)). Information regarding the identified potential issue(s) can be provided to a user.

Abstract: A heuristics-based concurrent edit detector (“ConE”) can notify collaborators about potential conflicts that may be caused by edits made by other collaborators. ConE may compare concurrent edits submitted by collaborators, calculate the extent of overlap between two sets of edits, apply one or more filters to balance recall versus precision, and decide whether to alert the collaborators about candidate potential conflicts. ConE may be light-weight and easily scalable to work in a very large environment with numerous collaborators.

Abstract: A data mining technique is used to find large frequently-occurring source code patterns from methods/APIs that can be used in code development. Simplified trees that represent the syntactic structure and type and method usage of a source code fragment, such as a method, are mined to find closed and maximal frequent subtrees which represent the largest frequently-occurring source code patterns or idioms associated with a particular type and method usage. These idioms are then used in an idiom web service and/or a code completion system to assist users in the development of source code programs.

Abstract: A data mining technique is used to find large frequently-occurring source code patterns from methods/APIs that can be used in code development. Simplified trees that represent the syntactic structure and type and method usage of a source code fragment, such as a method, are mined to find closed and maximal frequent subtrees which represent the largest frequently-occurring source code patterns or idioms associated with a particular type and method usage. These idioms are then used in an idiom web service and/or a code completion system to assist users in the development of source code programs.

Abstract: A heuristics-based concurrent edit detector (“ConE”) can notify collaborators about potential conflicts that may be caused by edits made by other collaborators. ConE may compare concurrent edits submitted by collaborators, calculate the extent of overlap between two sets of edits, apply one or more filters to balance recall versus precision, and decide whether to alert the collaborators about candidate potential conflicts. ConE may be light-weight and easily scalable to work in a very large environment with numerous collaborators.

Abstract: A natural language code search service provides idioms or frequently-occurring code patterns for a code fragment based on similar type usage and method/API invocation usage. The search service uses a data mining technique that mines code snippets found from various websites and code snippets generated from a neural model to detect idioms in the code snippets that were previously unknown and which can be reused. A search is initiated through a natural language query within a code development tool or application thereby avoiding the need to switch out of the current application to perform the search.

Abstract: A data mining technique is used to find large frequently-occurring source code patterns from methods/APIs that can be used in code development. Simplified trees that represent the syntactic structure and type and method usage of a source code fragment, such as a method, are mined to find closed and maximal frequent subtrees which represent the largest frequently-occurring source code patterns or idioms associated with a particular type and method usage. These idioms are then used in an idiom web service and/or a code completion system to assist users in the development of source code programs.

Abstract: Described herein is a system and method for detecting correlated changes (e.g., between code files and configuration files). For a plurality of code files and a plurality of configuration files, a correlated change model is trained to identify correlated changes across the code files and the configuration files using a machine learning algorithm that discovers change rules using a support parameter, and, a confidence parameter, and, a refinement algorithm that refines the discovered change rules. The correlated change model comprising the change rules is stored. The correlated change model can be used to identify potential issue(s) regarding a particular file (e.g., changed code or configuration file(s)). Information regarding the identified potential issue(s) can be provided to a user.

Abstract: Techniques to identify idiomatic code in a code base are described. Embodiments of such techniques are configured with idiom information corresponding to idiomatic code representations of computer code of which each idiomatic code representation comprises information corresponding to a control structure and variable usage. These techniques are operative to compare the idiomatic code representations to computer code fragments in the code base and identify one or more code fragments matching at least one of the idiomatic code representations. These techniques may identify functional operators for replacing the code fragments in the code base. Other embodiments are described and claimed.

Abstract: Techniques to identify idiomatic code in a code base are described. Embodiments of such techniques are configured with idiom information corresponding to idiomatic code representations of computer code of which each idiomatic code representation comprises information corresponding to a control structure and variable usage. These techniques are operative to compare the idiomatic code representations to computer code fragments in the code base and identify one or more code fragments matching at least one of the idiomatic code representations. These techniques may identify functional operators for replacing the code fragments in the code base. Other embodiments are described and claimed.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.

Abstract: Techniques pertaining to analyzing power consumed by a processing unit in a mobile computing device caused by execution of certain modules are described herein. A power trace is generated that indicates an amount of power consumed by the processing unit over time, and the power trace is aligned with an execution log. Spikes are extracted from the power trace, and computing operations are performed over the spikes to acquire data pertaining to power consumed by the processing unit that are attributable to modules in the execution log.