Relationship between Module Size, Alternative Cost and Bugs

Selle lõputöö eesmärgiks on uurida, kas alternatiivkulu (AC) ja mooduli suurus viivad suurema vigade arvuni tarkvaraprojektis. Kasutades nelja tarkvaraprojekti – JQuery, Font-Awesome, ReactJS ja Atom – versiooniajaloost ja vearaportitest eraldatud andmeid, arvutame me nende alternatiivkulud. Seejärel kasutame me Kendalli korrelatsiooni, et uurida AC ja vigade ning mooduli suuruse (mõõdetuna koodiridades) ja vigade vahelise seose tugevust. Me leidsime, et moodulite suuruse ja vigade vahel on tugev korrelatsioon kõigis neljas tarkvaraprojektis. Samas AC ja vigade vaheline seos jäi tõendamata. Oma uurimusest järeldame, et tarkvaraprojekti kvaliteeditagamise tegevuste käigus tuleks suurtele moodulitele pöörata rohkem tähelepanu. Alternatiivkulu ei ole oluline vigade asukoha tuvastamiseks.The aim of this thesis is to find out if Alternative Cost (AC) and size of modules lead to more bugs in a software project. Using the historical churn extracted from revisions data and bug reports data retrieved from four software projects namely, JQuery, Font-Awesome, ReactJS, and Atom, we calculate their AC. After which we use Kendall correlation to investigate the strength of association between AC and bugs, and module size (measured in Lines of Code) and bugs. We find a strong association between size of modules in all four software projects and bugs existing in them, while that of AC and bugs remain inconclusive. From our investigation, we conclude that when quality assurance activities are performed on a software project, modules with larger size should be given more attention. On the other hand, using our result, Alternative Cost is not relevant for bugs localization

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models. To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning. We present the \emph{BugHunter Dataset}: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug's presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74