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SLIM-DataManager for Software Performance Measurement

SLIM gives you the power to build and analyze a vast collection of completed software projects.

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Completed Software Project Core and Custom Metric Database

Are you still capturing project metrics in spreadsheets–with little ability to share data across the enterprise? Are different teams capturing different metrics with no standardization? Can you add new metrics and scale your corporate repository as your analytical needs expand?

QSM’s SLIM-DataManager will meet all your enterprise data repository requirements.

SLIM-DataManager is a robust data repository tool, included with the QSM SLIM Suite, that helps create a corporate database of your completed project history. Once implemented, SLIM-DataManager works hand-in-hand with the full complement of SLIM products to analyze your data, uncover key relationships and trends, and support fact-based estimation.

SLIM-DataManager

Completed software project core and custom metric data

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SLIM-Control
Software project actual data from the closeout process
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SLIM-DataManager
Central repository of historical software project data
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SLIM-Metrics
Software productivity and performance analytics

How It Fits in the SLIM Ecosystem

Growing and maintaining historical data for completed software projects provides the intelligence behind successful software projects. SLIM-DataManager is simple to use, yet powerful, equipping you to establish a software metrics program tailored to your needs. Historical data is the best source of inputs for software project estimates.  It can be used to validate internal and vendor estimates.

Recording actual software performance data while the project is underway makes project closeout a snap! Import SLIM-Control plan and actual core and custom metric data into SLIM-DataManager. Review cost and schedule overruns, scope growth/reduction, and record factors that had positive or negative effects on the outcomes. Import historical data to validate in-progress software project forecasts.

Data analytics that describe key relationships among software project outcomes, such as variations in schedules with scope, train SLIM’s machine learning algorithms, and guide your process improvement goals.  SLIM-Metrics links to your SLIM-DataManager database. Use SLIM-Metrics to easily define queries to create datasets by division, industry, application type, or other characteristics to understand key aspects of your development environment, perform regression analysis, and create reference trends used on other SLIM software.
 

Key Features

Completed Software Project Data

Historical software product data is vital for generating realistic estimates of future projects or releases, and performance benchmarking.

  • Just three core metrics — actual Software Size, Development & Test Duration, and Development & Test Effort — are needed to calculate SLIM's Productivity Index (PI)
  • Enter project characteristics and demographic data to support the statistical analysis of factors that have a high degree of influence on software project performance, such as application type, development methodology (Waterfall, Agile, other), or contract type
  • Use data entry tabs to capture metric data for a small number of projects or leverage SLIM's API and Data Importer utility
Historical software project data entry screen with multiple tabs; Basic Info tab for core metrics highlighted

Custom Software Metrics

Take advantage of over 300 pre-defined common software metrics or define an unlimited number of custom metrics such as software process maturity level, testing methods, or software security requirements and standards.  Five metric types are available:

  • Text
  • Numeric
  • Date
  • Single-Selection
  • Multi-Selection
screen to define and organize multiple software project custom metrics

User Defined Variables

Calculate variables from up to five metrics using easily defined formulas and functions.

Examples include:

  • Percent Test Coverage
  • Test Cases Successful
  • Average Sprint Velocity
  • Average Sprints / Release
Screen for calculating user defined metrics for text, numeric, single-select, multi-select, and date metrics

Software Project Closeout Review

Compare original software project estimates to actual project outcomes:

  • Time
  • Effort
  • Cost 
  • Max Peak Staff
  • Requirements Growth or Reduction

Record and rate factors that had positive or negative impacts on the project

Screen to enter time, effort, cost, and staff overruns compared to plan plus reductions or growth of requirements

SLIM-DataManager Benefits

Source of software estimate assumptions

Get estimated values for software size, productivity, effort, duration, and team sizes from similar completed projects

Validate software estimates

Validate proposed software project outcomes against actual performance from history

Tune SLIM predictive model

Adjust SLIM's estimation model using historical time and effort data 

Data quality checks

Perform data validation checks to ensure proper data entry and eliminate duplicate records

Request a Demo

Register for a live demo of SLIM to see how data-driven software estimation and analytics can improve the way your organization plans and delivers software projects. During the session, you’ll see how SLIM uses proven models and industry benchmark data to help teams produce more accurate estimates, manage in-flight projects, and conduct post-project analysis.