Merge pull request #553 from mlte-team/mlte-514

MLTE-514

demo/scenarios/1_requirements.ipynb

@@ -527,24 +527,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In MLTE, we define requirements by constructing a specification (`Spec`). For each property, we define the validations to perform as well. Note that several new `Value` types (`MultipleAccuracy`, `RankSums`, `MultipleRanksums`) had to be created to define the validation methods that will validate each Condition."
+    "In MLTE, we define requirements by constructing a specification (`Spec`). For each QA category, we define the validations to perform as well. Note that several new `Value` types (`MultipleAccuracy`, `RankSums`, `MultipleRanksums`) had to be created to define the validation methods that will validate each Condition."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from mlte.spec.spec import Spec\n",
     "\n",
     "# The Properties we want to validate, associated with our scenarios.\n",
-    "from mlte.property.costs.storage_cost import StorageCost\n",
-    "from mlte.property.fairness.fairness import Fairness\n",
-    "from mlte.property.robustness.robustness import Robustness\n",
-    "from mlte.property.interpretability.interpretability import Interpretability\n",
-    "from mlte.property.costs.predicting_memory_cost import PredictingMemoryCost\n",
-    "from mlte.property.costs.predicting_compute_cost import PredictingComputeCost\n",
+    "from mlte.qa_category.costs.storage_cost import StorageCost\n",
+    "from mlte.qa_category.fairness.fairness import Fairness\n",
+    "from mlte.qa_category.robustness.robustness import Robustness\n",
+    "from mlte.qa_category.interpretability.interpretability import Interpretability\n",
+    "from mlte.qa_category.costs.predicting_memory_cost import PredictingMemoryCost\n",
+    "from mlte.qa_category.costs.predicting_compute_cost import PredictingComputeCost\n",
     "\n",
     "# The Value types we will use to validate each condition.\n",
     "from mlte.measurement.storage import LocalObjectSize\n",
@@ -562,9 +562,9 @@
     "from demo.scenarios.values.string import String\n",
     "\n",
     "\n",
-    "# The full spec. Note that the Robustness Property contains conditions for both Robustness scenarios.\n",
+    "# The full spec. Note that the Robustness QACategory contains conditions for both Robustness scenarios.\n",
     "spec = Spec(\n",
-    "    properties={\n",
+    "    qa_categories={\n",
     "        Fairness(\n",
     "            \"Important check if model performs well accross different populations\"\n",
     "        ): {\n",

demo/scenarios/2_evidence.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "## 2. Collect Evidence\n",
     "\n",
-    "In the second phase of SDMT, we collect _evidence_ to attest to the fact that the model realized the properties specified in the previous phase.\n",
+    "In the second phase of SDMT, we collect _evidence_ to attest to the fact that the model realized the QA categories specified in the previous phase.\n",
     "\n",
     "We define and instantiate `Measurement`s to generate this evidence. Each individual piece of evidence is a `Value`. Once `Value`s are produced, we can persist them to an _artifact store_ to maintain our evidence across sessions. "
    ]

demo/scenarios/properties/accuracy.py

@@ -1,17 +1,17 @@
-from mlte.property.base import Property
+from mlte.qa_category.base import QACategory
 
 
-class Accuracy(Property):
+class Accuracy(QACategory):
     """
-    The Accuracy property reflects the overall functional accuracy of a model.
+    The Accuracy QA category reflects the overall functional accuracy of a model.
     """
 
     def __init__(self, rationale: str):
         """Initialize a Accuracy instance."""
         super().__init__(
             instance=self,
             description="""
-                The Accuracy property assesses the total predictive performance requirements of a
+                The Accuracy QA category assesses the total predictive performance requirements of a
                 model and system. These requirements are typically given as a single real valued number. 
                 """,
             rationale=rationale,

demo/scenarios/properties/interoperability.py

@@ -1,18 +1,18 @@
-from mlte.property.base import Property
+from mlte.qa_category.base import QACategory
 
 
-class Interoperability(Property):
+class Interoperability(QACategory):
     """
-    The Interoperability property reflects contract requirements on model interfaces.
+    The Interoperability QA category reflects contract requirements on model interfaces.
     """
 
     def __init__(self, rationale: str):
         """Initialize a Interoperability instance."""
         super().__init__(
             instance=self,
             description="""
-                The Interoperability property evaluates the requirements on the interfaces to a model and system. 
-                These requirements may be expressed in a variety of ways,
+                The Interoperability QA category evaluates the requirements on the 
+                interfaces to a model and system. These requirements may be expressed in a variety of ways,
                 including requriements on line formats in log files, timestamps, and log content. 
                 """,
             rationale=rationale,

demo/scenarios/properties/monitorability.py

@@ -1,17 +1,17 @@
-from mlte.property.base import Property
+from mlte.qa_category.base import QACategory
 
 
-class Monitorability(Property):
+class Monitorability(QACategory):
     """
-    The Monitorability property reflects  monitoring requirements.
+    The Monitorability QA category reflects  monitoring requirements.
     """
 
     def __init__(self, rationale: str):
         """Initialize a Monitorability instance."""
         super().__init__(
             instance=self,
             description="""
-                The Monitorability property assesses the monitoring requirements of a
+                The Monitorability QA category assesses the monitoring requirements of a
                 model and system. These requirements may be expressed in a variety of ways,
                 including requriements on line formats in log files, timestamps, and log content. 
                 """,

demo/scenarios/properties/resilience.py

@@ -1,19 +1,19 @@
-from mlte.property.base import Property
+from mlte.qa_category.base import QACategory
 
 
-class Resilience(Property):
+class Resilience(QACategory):
     """
-    The Resilience property reflects ...
+    The Resilience QA category reflects ...
     """
 
     def __init__(self, rationale: str):
         """Initialize a Resilience instance."""
         super().__init__(
             instance=self,
             description="""
-                The Resilience property assesses if a component is able to continue to carry out its mission in 
-                the face of adversity (i.e., if it provides required capabilities despite excessive stresses 
-                that can cause disruptions).
+                The Resilience QA category assesses if a component is able to continue
+                to carry out its mission in the face of adversity (i.e., if it provides required
+                capabilities despite excessive stresses that can cause disruptions).
                 """,
             rationale=rationale,
         )

demo/simple/evidence.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "## 2. Collect Evidence\n",
     "\n",
-    "In the second phase of SDMT, we collect _evidence_ to attest to the fact that the model realized the properties specified in the previous phase.\n",
+    "In the second phase of SDMT, we collect _evidence_ to attest to the fact that the model realized the QA categories specified in the previous phase.\n",
     "\n",
     "We define and instantiate `Measurement`s to generate this evidence. Each individual piece of evidence is a `Value`. Once `Value`s are produced, we can persist them to an _artifact store_ to maintain our evidence across sessions. "
    ]

demo/simple/requirements.ipynb

@@ -45,21 +45,21 @@
    "source": [
     "#### Build a `Specification`\n",
     "\n",
-    "In MLTE, we define requirements by constructing a specification (`Spec`). For each property, we define the validations to perform as well."
+    "In MLTE, we define requirements by constructing a specification (`Spec`). For each QA category, we define the validations to perform as well."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from mlte.spec.spec import Spec\n",
     "\n",
-    "from mlte.property.costs.storage_cost import StorageCost\n",
-    "from mlte.property.costs.training_memory_cost import TrainingMemoryCost\n",
-    "from mlte.property.costs.training_compute_cost import TrainingComputeCost\n",
-    "from mlte.property.functionality.task_efficacy import TaskEfficacy\n",
+    "from mlte.qa_category.costs.storage_cost import StorageCost\n",
+    "from mlte.qa_category.costs.training_memory_cost import TrainingMemoryCost\n",
+    "from mlte.qa_category.costs.training_compute_cost import TrainingComputeCost\n",
+    "from mlte.qa_category.functionality.task_efficacy import TaskEfficacy\n",
     "\n",
     "\n",
     "from mlte.measurement.storage import LocalObjectSize\n",
@@ -70,7 +70,7 @@
     "from mlte.value.types.image import Image\n",
     "\n",
     "spec = Spec(\n",
-    "    properties={\n",
+    "    qa_categories={\n",
     "        TaskEfficacy(\n",
     "            \"Important to understand if the model is useful for this case\"\n",
     "        ): {\n",

docs/docs/using_mlte.md

@@ -38,8 +38,8 @@ A `Specification` (or `Spec`) represents the requirements the model must meet in
 
 ```python
 from mlte.spec.spec import Spec
-from mlte.property.costs.storage_cost import StorageCost
-from mlte.property.fairness.fairness import Fairness
+from mlte.qa_category.costs.storage_cost import StorageCost
+from mlte.qa_category.fairness.fairness import Fairness
 
 from mlte.measurement.storage import LocalObjectSize
 from demo.scenarios.values.multiple_accuracy import MultipleAccuracy
@@ -198,10 +198,10 @@ A `Spec` represents the requirements and corresponding thresholds (or validators
 from mlte.spec.spec import Spec
 
 # The Properties we want to validate, associated with our scenarios.
-from mlte.property.costs.storage_cost import StorageCost
-from mlte.property.fairness.fairness import Fairness
-from mlte.property.robustness.robustness import Robustness
-from mlte.property.costs.predicting_memory_cost import PredictingMemoryCost
+from mlte.qa_category.costs.storage_cost import StorageCost
+from mlte.qa_category.fairness.fairness import Fairness
+from mlte.qa_category.robustness.robustness import Robustness
+from mlte.qa_category.costs.predicting_memory_cost import PredictingMemoryCost
 
 # The Value types we will use to validate each condition.
 from mlte.measurement.storage import LocalObjectSize

mlte/catalog/model.py

@@ -53,11 +53,11 @@ class CatalogEntry(Filtrable):
     tags: List[str] = []
     """Tags for the problem for the entry."""
 
-    property_category: Optional[str] = None
-    """The property category or QA for the entry."""
+    qa_category: Optional[str] = None
+    """The QA category for the entry."""
 
-    property: Optional[str] = None
-    """The property for the entry."""
+    quality_attribute: Optional[str] = None
+    """The quality attribute for the entry."""
 
     code_type: CatalogEntryType
     """The code type the entry."""

mlte/frontend/nuxt-app/assets/schema/artifact/spec/v0.0.1/schema.json

@@ -1,7 +1,7 @@
 {
   "$defs": {
     "ConditionModel": {
-      "description": "A description of a condition for a property.",
+      "description": "A description of a condition for a QACategory.",
       "properties": {
         "name": {
           "title": "Name",
@@ -30,8 +30,8 @@
       "title": "ConditionModel",
       "type": "object"
     },
-    "PropertyModel": {
-      "description": "A description of a property.",
+    "QACategoryModel": {
+      "description": "A description of a quality attribute category.",
       "properties": {
         "name": {
           "title": "Name",
@@ -78,7 +78,7 @@
         "name",
         "module"
       ],
-      "title": "PropertyModel",
+      "title": "QACategoryModel",
       "type": "object"
     }
   },
@@ -93,13 +93,13 @@
       "title": "Artifact Type",
       "type": "string"
     },
-    "properties": {
-      "type": "array",
+    "qa_categories": {
+      "default": [],
       "items": {
-        "$ref": "#/$defs/PropertyModel"
+        "$ref": "#/$defs/QACategoryModel"
       },
-      "default": [],
-      "title": "Properties"
+      "title": "Qa Categories",
+      "type": "array"
     }
   },
   "title": "SpecModel",

mlte/frontend/nuxt-app/assets/schema/artifact/validated/v0.0.1/schema.json

@@ -1,7 +1,7 @@
 {
   "$defs": {
     "ConditionModel": {
-      "description": "A description of a condition for a property.",
+      "description": "A description of a condition for a QACategory.",
       "properties": {
         "name": {
           "title": "Name",
@@ -74,8 +74,8 @@
       "title": "Identifier",
       "type": "object"
     },
-    "PropertyModel": {
-      "description": "A description of a property.",
+    "QACategoryModel": {
+      "description": "A description of a quality attribute category.",
       "properties": {
         "name": {
           "title": "Name",
@@ -122,7 +122,7 @@
         "name",
         "module"
       ],
-      "title": "PropertyModel",
+      "title": "QACategoryModel",
       "type": "object"
     },
     "ResultModel": {
@@ -167,13 +167,13 @@
           "title": "Artifact Type",
           "type": "string"
         },
-        "properties": {
-          "type": "array",
+        "qa_categories": {
+          "default": [],
           "items": {
-            "$ref": "#/$defs/PropertyModel"
+            "$ref": "#/$defs/QACategoryModel"
           },
-          "default": [],
-          "title": "Properties"
+          "title": "Qa Categories",
+          "type": "array"
         }
       },
       "title": "SpecModel",

mlte/frontend/nuxt-app/assets/schema/catalog/catalog_entry/v0.0.1/schema.json

@@ -97,7 +97,7 @@
       "title": "Tags",
       "type": "array"
     },
-    "property_category": {
+    "qa_category": {
       "anyOf": [
         {
           "type": "string"
@@ -107,9 +107,9 @@
         }
       ],
       "default": null,
-      "title": "Property Category"
+      "title": "Qa Category"
     },
-    "property": {
+    "quality_attribute": {
       "anyOf": [
         {
           "type": "string"
@@ -119,7 +119,7 @@
         }
       ],
       "default": null,
-      "title": "Property"
+      "title": "Quality Attribute"
     },
     "code_type": {
       "$ref": "#/$defs/CatalogEntryType"

mlte/frontend/nuxt-app/components/FormFields/system-requirements.vue

@@ -36,11 +36,11 @@
       </p>
       <UsaTextInput v-model="requirement.quality">
         <template #label>
-          <b>System Quality:</b> What is the model property to be tested, such
+          <b>System Quality:</b> What is the model quality attribute category to be tested, such
           as accuracy, performance, robustness, fairness, or resource
           consumption?
           <InfoIcon>
-            Property by which the model will be evaluated in the context of the
+            Quality attribute category by which the model will be evaluated in the context of the
             system <br />
             (e.g., Accuracy, Performance, Robustness, Fairness, Resource
             Consumption).
@@ -54,7 +54,7 @@
       <UsaTextInput v-model="requirement.stimulus">
         <template #label>
           <b>Stimulus:</b> What is the input to the model, the action, or the
-          event that will enable testing of the property, such as input data,
+          event that will enable testing of the quality attribute category, such as input data,
           system event, or user operation?
           <InfoIcon>
             A condition arriving at the system/model (e.g., data,