International Business Machines Corporation

Marek Franczyk

Lukasz Grzegorz Cmielowski

Andrzej Jan Wrobel

Tymoteusz Gedliczka

Robert Karcz

Embodiments include a system for release cycle optimization; the system includes a processor configured to perform a method. The method includes accessing, by a processor, historical data relating to a plurality of software version each having a plurality of attributes; selecting a subset of attributes from the plurality of attributes; receiving a set of data values for each of the subset of attributes from the plurality of attributes; performing one or more simulations of a software development cycle utilizing the set of data values; and obtaining a set of results from the one or more simulations comprising a plurality of predicted field defects values corresponding to each of the set of data values.

Release cycle optimization based on significant features values simulation

The disclosed herein relates to a method for failure rate prediction of a feature of a system under development. The method is executed by a processor coupled to a memory. The method includes defining a feature state of the feature during a predetermined time interval, the predetermined time interval being associated with a development stage of the system. The method also includes assigning a first defect class value to the feature for the predetermined time interval, the first defect class value configured to indicate a first condition and selecting, when a defect is reported for the feature, a second defect class value indicating a second condition, the second condition being associated with a higher failure rate than the first condition. The method can be embodied in system and a computer program product.

Training set creation for classifying features of a system under agile development

A method for performing software error detection and prediction. The method includes identifying a plurality of software components in a computer software product. For each of the software components of the plurality of software components, the risk-relevant historical data pertaining to the respective software component is measured, then classified into at least a set of risk-increasing data and a set of risk-decreasing data. The set of risk-increasing data and the set of risk-decreasing data are then normalized, and a failure risk value for the respective software component is calculated by subtracting a weighted sum of the normalized values for the risk-decreasing data from a weighted sum of the normalized values for the risk-increasing data.

Growing community of inventors

Bojszowy, Poland

Marek Franczyk