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class Frame:
    def __init__(self, tracer):
        self.tracer = tracer
    def __enter__(self):
            set things up
            return thing
    def __exit__(self, type, value, traceback):
        self.tracer.pop()

class StackTracer

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{"name": "root", "value": 3.793658971786499, "children": [{"name": "revenu_disponible<2017>", "value": 3.7249960899353027, "children": [{"name": "pensions_nettes<2017>", "value": 0.4288208484649658, "children": [{"name": "chomage_net<2017-01>", "value": 0.38701677322387695, "children": [{"name": "chomage_imposable<2017-01>", "value": 0.38640856742858887, "children": [{"name": "chomage_brut<2017-01>", "value": 4.673004150390625e-05, "children": []}, {"name": "csg_deductible_chomage<2017-01>", "value": 0.38626861572265625, "children": [{"name": "chomage_brut<2017-01>", "value": 1.811981201171875e-05, "children": []}, {"name": "csg_imposable_chomage<2017-01>", "value": 0.0004315376281738281, "children": [{"name": "chomage_brut<2017-01>", "value": 1.7404556274414062e-05, "children": []}]}, {"name": "taux_csg_remplacement<2017-01>", "value": 0.38537049293518066, "children": [{"name": "rfr<2015>", "value": 0.38170957565307617, "children": [{"name": "abattements_plus_values<2015>", "value": 0.0002639293670654297, "c

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{"name": "simulation", "value": 13.282548189163208, "children": [{"name": "revenu_disponible<2017>", "value": 13.213572263717651, "children": [{"name": "pensions_nettes<2017>", "value": 0.47104573249816895, "children": [{"name": "chomage_net<2017-01>", "value": 0.426302433013916, "children": [{"name": "chomage_imposable<2017-01>", "value": 0.4256303310394287, "children": [{"name": "chomage_brut<2017-01>", "value": 6.246566772460938e-05, "children": []}, {"name": "csg_deductible_chomage<2017-01>", "value": 0.42541027069091797, "children": [{"name": "chomage_brut<2017-01>", "value": 2.6941299438476562e-05, "children": []}, {"name": "csg_imposable_chomage<2017-01>", "value": 0.0004298686981201172, "children": [{"name": "chomage_brut<2017-01>", "value": 2.47955322265625e-05, "children": []}]}, {"name": "taux_csg_remplacement<2017-01>", "value": 0.4244725704193115, "children": [{"name": "rfr<2015>", "value": 0.4200258255004883, "children": [{"name": "abattements_plus_values<2015>", "value": 0.00045013427734375, "c

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{"name": "simulation", "value": 3.9890100955963135, "children": [{"name": "ppa<2018-05>", "value": 3.9890100955963135, "children": [{"name": "ppa_eligibilite_etudiants<2018-05>", "value": 2.4416704177856445, "children": [{"name": "rsa_majore_eligibilite<2018-05>", "value": 1.052875280380249, "children": [{"name": "en_couple<2018-05>", "value": 0.00028967857360839844, "children": [{"name": "nb_parents<2018-05>", "value": 0.00017762184143066406, "children": []}]}, {"name": "rsa_isolement_recent<2018-05>", "value": 6.079673767089844e-05, "children": []}, {"name": "age<2018-05>", "value": 0.00018477439880371094, "children": [{"name": "date_naissance<2018-05>", "value": 4.124641418457031e-05, "children": []}]}, {"name": "autonomie_financiere<2018-05>", "value": 0.839031457901001, "children": [{"name": "salaire_net<2017-11>", "value": 0.4517393112182617, "children": [{"name": "salaire_imposable<2017-11>", "value": 0.45074033737182617, "children": [{"name": "salaire_de_base<2017-11>", "value": 5.91278076171875e-05, 

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The IBM Systems Science Institute

Rubric: Software Engineering : Factual Claims : Defect Cost Increase : Pressman Ratios

Context

Background: I have been researching quantity and quality of empirical evidence underlying claims in software engineering. What do we know, and how well-established is that? See in particular https://leanpub.com/leprechauns which concludes that the answer is in (too) many cases "not much, and poor".

This applies in particular to the "Defect Cost Increase" claim, which is poorly supported by evidence. The claim states that the longer a defect stays undiscovered after being introduced in a software system's artefacts, the more expensive it is to correct.

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The citations game: Pressman Ratios

Rubric: Software Engineering : Factual Claims : Defect Cost Increase : Pressman Ratios

Context

See previous note on the IBM Systems Sciences Institute

Origins

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The citations game: Wolverton Ratios

Rubric: Software Engineering : Factual Claims : Defect Cost Increase : Wolverton Ratios

Context

See previous note on the IBM Systems Sciences Institute

In absolute numbers, the Wolverton are as follows: 139:455:977:7136:14102, claimed dollar costs of fixing an "average" defect. (Itself an absurd claim, see Leprechauns, I should perhaps write more on that.)

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1. What do you want to do at Alan? How do you see your ideal role?

I'll be answering this in two parts - what I'd want to do anywhere quite generically, and what might make me feel happy at Alan, given what I know about the organization.

Perhaps the biggest thing I intend to do, in any professional role, is to "bring my whole self to work", and expect and encourage others around me to do the same. I've long felt that the software industry tends to get too hung up on narrowly defined "roles" - developer, tester, architect, data person, and so on. For me, each person fits the Whitman quote, "I am vast, I contain multitudes". We each have a variety of resources we can draw upon, developed from past professional experience or from personal life - as a parent, a spouse, a friend, a volunteer, and so on - or from how other people or even fictional characters have inspired us.

For instance, while I primarily think of myself as a developer, one of my facets consists of team "coaching" tools. One such tool that I

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The citations game: Cone of Uncertainty

Rubric: Software Engineering : Factual Claims : Cone of Uncertainty : Empirical Validation

Context

The Wikipedia page on "Cone of Uncertainty" lists two "empirical validations" of Boehm's initial work. I didn't chase these down for Leprechauns. They are:

• "Later work by Boehm and his colleagues at USC applied data from a set of software projects from the U.S. Air Force and other sources to validate the model;"
• "The basic model was further validated based on work at NASA's Software Engineering Lab (NASA 1990 p. 3-2)."

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OpenFisca et "métaprogrammation"

rfr = individu.foyer_fiscal('rfr', period.n_2)

L'appel ci-dessus est à peu près équivalent à

simulation = individu.simulation