When any one or a combination of a ticket, a source code change history, and a source code is input, a source code analysis apparatus generates information of a factor pattern from the information, and a factor metrics calculation unit calculates factor metrics regarding the factor pattern as measured information regarding the factor pattern. In addition, the QCD measurement value is acquired from the information, the QCD index is calculated based on the QCD measurement value, and the correlation calculation unit calculates a correlation coefficient for a pair of the factor metrics and the QCD index, stores the correlation coefficient in the correlation list, calculates a score for the pair of the factor pattern and the QCD measurement value, and generates the important factor pattern information table in which the score is assigned to the pair of the factor pattern and the QCD measurement value.

When any one or a combination of a ticket, a source code change history, and a source code is input, a source code analysis apparatus generates information of a factor pattern from the information, and a factor metrics calculation unit calculates factor metrics regarding the factor pattern as measured information regarding the factor pattern. In addition, the QCD measurement value is acquired from the information, the QCD index is calculated based on the QCD measurement value, and the correlation calculation unit calculates a correlation coefficient for a pair of the factor metrics and the QCD index, stores the correlation coefficient in the correlation list, calculates a score for the pair of the factor pattern and the QCD measurement value, and generates the important factor pattern information table in which the score is assigned to the pair of the factor pattern and the QCD measurement value.

A method of a base scorer in a scoring service container includes sending a model identifier to a model loader of an application specific scorer in the scoring service container, receiving a model object from the model loader in response to sending the model identifier, sending a request for a scoring from a client application to a scoring function of the application specific scorer, receiving the scoring from the application specific scorer, and returning the scoring to the client application.

Some embodiments provide a method for identifying runtime complexity of a policy. The method receives, through a user interface (UI), a set of code defining a particular policy. For each variable in the particular policy, the method identifies a first occurrence of the variable in the particular policy to determine a number of values assigned to the variable. Variables determined to be assigned one value are separated from variables determined to be assigned more than one value. Based on the determinations for each variable, the method calculates a set of metrics that include at least time complexity, size complexity, and count complexity for the particular policy. The method then displays, through the UI, the calculated set of metrics along with a set of one or more suggestions for optimizing the particular policy based on the calculated set of metrics.

Some embodiments provide a method for identifying runtime complexity of a policy. The method receives, through a user interface (UI), a set of code defining a particular policy. For each variable in the particular policy, the method identifies a first occurrence of the variable in the particular policy to determine a number of values assigned to the variable. Variables determined to be assigned one value are separated from variables determined to be assigned more than one value. Based on the determinations for each variable, the method calculates a set of metrics that include at least time complexity, size complexity, and count complexity for the particular policy. The method then displays, through the UI, the calculated set of metrics along with a set of one or more suggestions for optimizing the particular policy based on the calculated set of metrics.

The invention concerns a software based system for computer-aided design (CAD) that includes user interface tailoring and methods for continuously evaluating the learning progress of the user and increase work productivity by searching for the patterns in the user input to predict the goal of user actions and propose next action to reach the goal in optimal way. Components of the presented invention relate to collection of the different user input including at least eye tracking and user focus and attention related features; analyzing continuously user's behavior to evaluate user learning progress and work productivity related to the computer-aided design tool; monitoring user interface components that are used by the user; searching for the patterns in user behavior; tailoring user interface controls to maximize a work productivity at the same time increasing user's qualification profile. The core of the invention comprises gaze tracking as an input component for better user activity and performance tracking, component for features extraction fusion of different types user input, continuously monitored users qualification profile and two classifiers making decision on user interface complexity level and a set of most relevant graphical user interface controls for the next user action.

A tool is provided for evaluating contributions of different software developers to a software project, which considers both the structural value and non-structural value of each developer's contributions. Source code for the project is parsed to generate abstract syntax trees (ASTs) representing the code at different times during the project. The tool may perform both a structural value analysis and a non-structural value analysis of the code contributions of each developer. The structural value analysis for each developer may include a call-commit analysis of a call-commit graph generated from the ASTs to analyze function calls and commits attributable to each developer. The non-structural value analysis for each software developer may include identifying and classifying commits attributable to each developer based on impact of such commits on the project. The tool may then calculate a development value score for each developer based on the structural and non-structural metrics for each developer.

Systems and methods for evaluation of entity health for a product are described. In some embodiments, a computing system can receive data defining values of a group of diagnostic signals pertaining to a domain of a product of an entity. The computing system can then generate attributes indicative of entity health condition in the domain by applying a machine-learning model to the data. The attributes can include a health reward parameter and a signal strength parameter. The computing system also can encode the health reward parameter in a particular marking according to a marking schema. The computing system can further cause presentation of the particular marking and/or the signal strength parameter.

A system and method for comparative performance monitoring of software release versions is disclosed. A remote network management platform may include a computational instance for managing a network. Transactions between a server of the computational instance and a client device in the managed network may be logged to a database. Transactions may be carried out by a release version of a set of program code units executing on the server. A software application executing on a computing device may retrieve and analyze a first set of transactions carried out by a first release version of the set of program code units to determine a first set of performance metrics, and do the same for a second set of transactions carried out by a second release version of the set of program code units to determine a second set of performance metrics. A classification filter may be applied to the metrics, and a quantitative comparison of the filtered first and second sets of performance metrics may be displayed on graphical user device.

Methods and systems for a platform development version control system for monitoring contributors to software platform development. The methods and systems generate data analytics on contributors to software platform development using group affiliations as listed in a group directory (e.g., a corporate directory for an entity providing the software platform) as a common organizing factor. For example, by organizing the methods and systems according to the group affiliations, the methods and systems may generate data analytics on contributions of contributors within those groups, irrespective of whether or not the group members are working on the same project. The methods and systems may then provide recommendations and graphical representations based on the data analytics.