Metrics for Recommending Corrective and Preventive Actions (CAPAs) in Software Development Projects: a Systematic Literature Review

This dataset is part of the systematic literature review (SLR) entitled “Metrics for Recommending Corrective and Preventive Actions (CAPAs) in Software Development Projects: a Systematic Literature Review”. The dataset represents the data collected from the studies we included in our final reading log. Specifically, we collected 3 types of relevant data. These are metrics, anomalies, and actions. In order to capture the relevance and significance of the data for our research, we also specified several attributes for each data type. The attributes we collected are as follows:

Study

Each primary study is represented by its ID.

Title

Study title.

Year of Publication

The year in which the study was published.

Authors

How many people participated in the study.

Raw metrics

The raw metric characterising the study.

Metrics

The metric/s under which we grouped similar raw metrics. This information can be found in the data file attached to the SLR.

Metrics formulas

The mathematical formula describing the metric.

Metric descriptions

The definition and description of the metric used.

Metric notes

Additional information about the symbols used in the formula or terms used in the description.

Possible Metric values

The range of values that the metric can have or the range of metric measurements.

Metric context

The contextual usage of the metric in the study.

Is the metric used for detecting Anomalies?

‘Yes’, whether the metric is used for detecting anomalies, otherwise it is ‘No’. We note here that some papers declared that specific metrics have been used for detecting anomalies, but these anomalies have not been identified. We set ‘Yes’ for this kind of data samples but next the attribute ‘Anomaly’ has not been set.

Anomalies

The anomaly obtained from the textual analysis of the study included in the reading log.

Anomaly Descriptions

Additional descriptive information about the anomaly in question.

Root causes

The root causes of the encountered anomaly/ies or of those triggered by actions.

Metric threshold

The value of the metric or its category (low or high), which triggered the relevant anomaly/action.

Is the anomaly handled?

‘Yes’, if the obtained anomaly is fixed, otherwise it is No’. We note here that some papers declared that specific anomalies have been fixed, but the corrective actions have not been identified in the study. We set ‘Yes’ in this attribute for this kind of data samples but the next attribute ‘Suggested Actions’ has not been set.

Suggested Actions

The action gathered from the study included in the reading log.

Action Category

This attribute specifies the type of the action obtained. The possible values for this attribute are ‘Corrective’, ‘Preventive’ and ‘Enhancement’. ‘Corrective’ actions represent the actions applied to fix or correct the anomaly, while ‘Preventive’ actions are used to avoid the recurrence of the anomalies in the future. ‘Enhancement’ actions are actions adopted to increase software quality.

Action Sources

This attribute captures the origin or source of the action obtained. The possible values for this attribute are ‘Experimental’, ‘Observational’ and ‘Industrial’. ‘Observational’ actions originated from authors’ observations, ‘Experimental’ actions were developed based on experiments conducted in labs whereas ‘Industrial’ actions were suggested based on experiments conducted by companies or in an industrial context.

Handling approaches

The approaches that are suggested and/or used for fixing the anomalies encountered.

Used ML methods

The machine learning algorithms used for detecting the anomalies.

Presence of action impact

‘Yes’, if the actions’ impact was measured and reported in the study, otherwise it was ‘No’.

Action impact metrics

The metric/s used for measuring actions’ impact. This captures how much improvement is perceived after applying the desired action.

Percentages of actions’ impact

The relative change obtained in the measurements of metrics’ impact before and after implementing the suggested action.