Abstract: A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.

Abstract: A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.

Abstract: A cost per unit of processed code metric is determined based on a quantity of processed code during a given time period that is attributable to one or more developers, and a cost to develop the quantity of processed code during the time period attributable to the one or more developers. Also, a quality scaling factor is calculated based on quality measurements for the quantity of processed code. Thereafter, the cost per unit of processed code metric is scaled (or discounted) by the quality scaling factor to provide a cost of development index (CDI). Because the inputs to the quality scaling factor may be determined in an automated fashion, this CDI determination permits more rapid response to any adverse assessments. Furthermore, because direct measurement of code quality is employed, more meaningful insight is provided into the likelihood that end product, i.e., the software code, is of good quality.

Abstract: A cost per unit of processed code metric is determined based on a quantity of processed code during a given time period that is attributable to one or more developers, and a cost to develop the quantity of processed code during the time period attributable to the one or more developers. Also, a quality scaling factor is calculated based on quality measurements for the quantity of processed code. Thereafter, the cost per unit of processed code metric is scaled (or discounted) by the quality scaling factor to provide a cost of development index (CDI). Because the inputs to the quality scaling factor may be determined in an automated fashion, this CDI determination permits more rapid response to any adverse assessments. Furthermore, because direct measurement of code quality is employed, more meaningful insight is provided into the likelihood that end product, i.e., the software code, is of good quality.