The Shape of Code

While it’s great that so much data was uncovered during the writing of the Evidence-based software engineering book, trying to locate data on a particular topic can be convoluted (not least because there might not be any). There are three sources of information about the data:

• the paper(s) written by the researchers who collected the data,
• my analysis and/or discussion of the data (which is frequently different from the original researchers),
• the column names in the csv file, i.e., data is often available which neither the researchers nor I discuss.

At the beginning I expected there to be at most a few hundred datasets; easy enough to remember what they are about. While searching for some data, one day, I realised that relying on memory was not a good idea (it was never a good idea), and started including data identification tags in every R file (of which there are currently 980+). This week has been spent improving tag consistency and generally tidying them up.

How might data identification information be extracted from the paper that was the original source of the data (other than reading the paper)?

Named-entity recognition, NER, is a possible starting point; after all, the data has names associated with it.

Tools are available for extracting text from pdf file, and 10-lines of Python later we have a list of named entities:

import spacy
 
# Load English tokenizer, tagger, parser, NER and word vectors
nlp = spacy.load("en_core_web_sm")
 
file_name = 'eseur.txt'
soft_eng_text = open(file_name).read()
soft_eng_doc = nlp(soft_eng_text)
 
for ent in soft_eng_doc.ents:
     print(ent.text, ent.start_char, ent.end_char,
           ent.label_, spacy.explain(ent.label_))

The catch is that en_core_web_sm is a general model for English, and is not software engineering specific, i.e., the returned named entities are not that good (from a software perspective).

An application domain language model is likely to perform much better than a general English model. While there are some application domain models available for spaCy (e.g., biochemistry), and application datasets, I could not find any spaCy models for software engineering (I did find an interesting word2vec model trained on Stackoverflow posts, which would be great for comparing documents, but not what I was after).

While it’s easy to train a spaCy NER model, the time-consuming bit is collecting and cleaning the text needed. I have plenty of other things to keep me busy. But this would be a great project for somebody wanting to learn spaCy and natural language processing 🙂

What information is contained in the undiscussed data columns? Or, from the practical point of view, what information can be extracted from these columns without too much effort?

The number of columns in a csv file is an indicator of the number of different kinds of information that might be present. If a csv is used in the analysis of X, and it contains lots of columns (say more than half-a-dozen), then it might be assumed that it contains more data relating to X.

Column names are not always suggestive of the information they contain, but might be of some use.

Many of the csv files contain just a few rows/columns. A list of csv files that contain lots of data would narrow down the search, at least for those looking for lots of data.

Another possibility is to group csv files by potential use of data, e.g., estimating, benchmarking, testing, etc.

More data is going to become available, and grouping by potential use has the advantage that it is easier to track the availability of new data that may supersede older data (that may contain few entries or apply to circumstances that no longer exist)

My current techniques for locating data on a given subject is either remembering the shape of a particular plot (and trying to find it), or using the pdf reader’s search function to locate likely words and phrases (and then look at the plots and citations).

Suggestions for searching or labelling the data, that don’t require lots of effort, welcome.