Principles of Pattern-Oriented Software Data Analysis

2016 is closing, and for 2017 we look forward to applying software diagnostics and software post-construction problem-solving insights gained over the 10 years of Software Diagnostics Institute research to software data analysis in general. In addition to memory snapshots (Dump Artefacts) and logs (Trace Artefacts) and their analysis that we abbreviated as DA+TA, we extend our pattern-oriented approach to additional artifacts as security data, source code, configuration data, telemetry, revision repositories, and stores. We consider all additional software data types as examples of generalized software narratives and traces and abbreviate as simply DATA.

It is time to bring together the principles of pattern-oriented data analysis:

1. Patterns-based

Data patterns and data analysis patterns are classified into catalogs and named to form pattern languages. Every major kind of software data and activity associated with its data analysis can have its own pattern language and catalog. Pattern catalogs are dynamic structures. New patterns are added, old are revised. New catalogs are refined, added, or combined. Pattern names change if necessary to accommodate new data meta-analysis insights (Pattern-Based Software Diagnostics). Patterns can be reused across different data domains.

2. Patterns-driven

Data patterns that are diagnosed using data analysis patterns that are guided by meta-patterns trigger appropriate actionable decisions (Pattern-Driven Software Diagnostics).

3. Systemic-based

Data analysis is a multidisciplinary activity incorporating insights from natural and medical sciences, humanities and social sciences (Systemic Software Diagnostics).

4. Mechanisms-based

The pattern-oriented data analysis may lead to data root cause analysis when coupled with mechanisms.

5. Narrative-based

Software data is a form of a software narrative including data analysis itself (the higher-order narrative analysis).

6. Pattern square-based

There are special and general data patterns and special and general data analysis patterns (Pattern Square).

7. Patterns-assisted

Since software usage is a human activity, software data analysis should be human-assisted. Data analysis patterns facilitate data analysis verifiability, elimination of data analysis errors, and provide independence of data analysis reporting from idiosyncratic data analysis habits*. Software data and data analysis patterns and their languages assist humans in achieving and maintaining software data analysis quality.

* B. Russo, The need for data analysis patterns (in software engineering), Perspectives on Data Science for Software Engineering (ISBN: 978-0128042069)