def index():
"Estimate Time and Prediction Interval (UPI, LPI)"
# use historical data of actual object size (LOC) and time to calculate
# development time based on planned LOC [HUMPHREY95] pp.153-155
#TODO: calculate Upper and Lower Prediction Interval
form = SQLFORM.factory(
Field("size",
"integer",
default=request.vars.planned_loc,
comment="Planned size (estimated LOC)"),
Field("prediction_interval",
"integer",
default="70",
requires=IS_INT_IN_RANGE(0, 100),
comment="Percentage (for LPI, UPI)"),
Field("project_id",
db.psp_project,
requires=IS_IN_DB(db, db.psp_project.project_id, "%(name)s"),
comment="Project to update plan"),
)
if form.accepts(request.vars, session):
# calculate regression parameters for historical LOC and tiem data:
actual_loc, hours = get_projects_metrics()
b0, b1 = calc_linear_regression(actual_loc, hours)
# get LOC planned size and calculate development time
size_k = form.vars.size
time_t = b0 + b1 * size_k
alpha = form.vars.prediction_interval / 100.0
redirect(
URL("update_plan",
args=form.vars.project_id,
vars={
'size_k': size_k,
'time_t': time_t,
'alpha': alpha
}))
return {'form': form}
def index():
"Estimate Time and Prediction Interval (UPI, LPI)"
# use historical data of actual object size (LOC) and time to calculate
# development time based on planned LOC [HUMPHREY95] pp.153-155
# TODO: calculate Upper and Lower Prediction Interval
form = SQLFORM.factory(
Field("size", "integer", default=request.vars.planned_loc, comment="Planned size (estimated LOC)"),
Field(
"prediction_interval",
"integer",
default="70",
requires=IS_INT_IN_RANGE(0, 100),
comment="Percentage (for LPI, UPI)",
),
Field(
"project_id",
db.psp_project,
requires=IS_IN_DB(db, db.psp_project.project_id, "%(name)s"),
comment="Project to update plan",
),
)
if form.accepts(request.vars, session):
# calculate regression parameters for historical LOC and tiem data:
actual_loc, hours = get_projects_metrics()
b0, b1 = calc_linear_regression(actual_loc, hours)
# get LOC planned size and calculate development time
size_k = form.vars.size
time_t = b0 + b1 * size_k
alpha = form.vars.prediction_interval / 100.0
redirect(
URL("update_plan", args=form.vars.project_id, vars={"size_k": size_k, "time_t": time_t, "alpha": alpha})
)
return {"form": form}
def linear_regression():
b0, b1 = calc_linear_regression(x_values, y_values)
return {'b0': b0, 'b1': b1, 'ok': round(b0,3)==-0.351 and round(b1,3)==0.095}