Infosys Limited

Infosys Technologies Limited

Girish Maskeri Rama

Santonu Sarkar

Avinash C Kak

Nagaraja Nanjegowda Siddaramappa

Shubha Ramachandran

Basavara Raju Muddu

Himanshu Tyagi

Thomas Weibel

Kenneth Heafield

Sree Aurovindh Viswanathan

Donggun Yoo

The invention relates to a system and method for identifying occurrences of preventive maintenance on computer source code. The method comprises analyzing source code, defining data sets associated with characteristics of the source code, and applying an operation algorithm to the data sets to thereby identify occurrences of preventive maintenance operations that were carried out on the source code. A first instance of a source code and a second instance of the source code are preferably analyzed, wherein the second instance of the source code is a revised version of the first instance of the source code, a first data set associated with the first instance of the source code and a second data set associated with the second instance of the source code are preferably defined, and the operation algorithm is preferably applied to the first data set and the second data set to thereby identify at least one occurrence of a preventive maintenance operation that was carried out on the source code.

System and method for detecting preventative maintenance operations in computer source code

The present invention provides a method and system for detecting a partial commit of software. A dependency information of the software is extracted from a version history and a bug database. A dimensional matrix containing a set of commit, and relationship information with a set of files with each commit is created from the dependency information. A centrality matrix is computed by performing a first set of matrix transformations on the dimensional matrix. A set of missing files of a partial commit, is identified by performing a second set of matrix transformations on the centrality matrix and a file vector, the file vector including a file dependency information of the partial commit.

Methods, systems and computer-readable media for detecting a partial commit

A method, non-transitory computer readable medium, and apparatus that obtains a version history for a plurality of source code files associated with the computer program. At least one defect probability value is generated for each of the plurality of source code files using at least one metric and the obtained version history. One or more of the plurality of source code files having a higher risk of including one or more defects than one or more other of the plurality of source code files are identified based on the generated defect probability values. Information regarding each of the one or more identified source code files is output.

Methods for predicting one or more defects in a computer program and devices thereof

Quality of modularization of source code is tested using different perspectives such as a structural modularity perspective, an architectural modularity perspective, a size perspective, and a similarity of purpose perspective. A history of changes in modularization may be kept such that the degree to which given source code is well-modularized can be determined over time. The changes made to the code by individual programmers may be kept, such that the degree to which their coding enhanced or harmed modularization may be tracked.

Evaluating programmer efficiency in maintaining software systems

A system, computer-implemented method, and computer-readable medium for determining the quality of an API includes receiving documentation of the API, determining one or more characteristics of the API based at least in part on the documentation of the API, determining or more measurement values based at least in part on the one or more characteristics of the API, and computing a quality score for the API based at least in part on the one or more measurement values.

Systems, methods, and computer-readable media for measuring quality of application programming interfaces

A system and method for improving modularity of a software source code is provided. The system comprises of a user interface for receiving source code; a source code model extractor for parsing and forming a model of the source code; a source code model database for storing the source code model, refactoring operators, and a record of refactoring changes; a modularity improvement analyzer for reading the source code model and modularity problem diagnosis data and generating a set of prescriptions; an optimal improvement suggestion selector for evaluating and selecting prescriptions; and a refactoring engine for receiving selected prescriptions and applying them on the source code.

System and method for improving modularity of large legacy software systems

Topics in source code can be identified using Latent Dirichlet Allocation (LDA) by receiving source code, identifying domain specific keywords from the source code, generating a keyword matrix, processing the keyword matrix and the source code using LDA, and outputting a list of topics. The list of topics is output as collections of domain specific keywords. Probabilities of domain specific keywords belonging to their respective topics can also be output. The keyword matrix comprises weighted sums of occurrences of domain specific keywords in the source code.

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Bengaluru, India

Girish Maskeri Rama