A Study of "Wheat" and "Chaff" in Source Code

Abstract

Natural language is robust against noise. The meaning of many sentences survives the loss of words, sometimes many of them. Some words in a sentence, however, cannot be lost without changing the meaning of the sentence. We call these words "wheat" and the rest "chaff." The word "not" in the sentence "I do not like rain" is wheat and "do" is chaff. For human understanding of the purpose and behavior of source code, we hypothesize that the same holds. To quantify the extent to which we can separate code into "wheat" and "chaff", we study a large (100M LOC), diverse corpus of real-world projects in Java. Since methods represent natural, likely distinct units of code, we use the, approximately, 9M Java methods in the corpus to approximate a universe of "sentences." We "thresh", or lex, functions, then "winnow" them to extract their wheat by computing the minimal distinguishing subset (MINSET). Our results confirm that programs contain much chaff. On average, MINSETS have 1.56 words (none exceeds 6) and comprise 4% of their methods. Beyond its intrinsic scientific interest, our work offers the first quantitative evidence for recent promising work on keyword-based programming and insight into how to develop powerful, alternative programming systems.