N. C. Shrikanth

Mehdi Bahrami, N. C. Shrikanth, Yuji Mizobuchi et al.

Code retrieval is allowing software engineers to search codes through a natural language query, which relies on both natural language processing and software engineering techniques. There have been several attempts on code retrieval from searching snippet codes to function codes. In this paper, we introduce Augmented Code (AugmentedCode) retrieval which takes advantage of existing information within the code and constructs augmented programming language to improve the code retrieval models' performance. We curated a large corpus of Python and showcased the the framework and the results of augmented programming language which outperforms on CodeSearchNet and CodeBERT with a Mean Reciprocal Rank (MRR) of 0.73 and 0.96, respectively. The outperformed fine-tuned augmented code retrieval model is published in HuggingFace at https://huggingface.co/Fujitsu/AugCode and a demonstration video is available at: https://youtu.be/mnZrUTANjGs .

Mehdi Bahrami, N. C. Shrikanth, Shade Ruangwan et al.

A large scale collection of both semantic and natural language resources is essential to leverage active Software Engineering research areas such as code reuse and code comprehensibility. Existing machine learning models ingest data from Open Source repositories (like GitHub projects) and forum discussions (like Stackoverflow.com), whereas, in this showcase, we took a step backward to orchestrate a corpus titled PyTorrent that contains 218,814 Python package libraries from PyPI and Anaconda environment. This is because earlier studies have shown that much of the code is redundant and Python packages from these environments are better in quality and are well-documented. PyTorrent enables users (such as data scientists, students, etc.) to build off the shelf machine learning models directly without spending months of effort on large infrastructure. The dataset, schema and a pretrained language model is available at: https://github.com/fla-sil/PyTorrent

N. C. Shrikanth, Tim Menzies

Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, perhaps a model learned from that region would suffice for the rest of the project. To support this claim, we offer a case study with 240 projects, where we find that the information in those projects "clump" towards the earliest parts of the project. A quality prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this "early bird" data, we can build models very quickly and very early in the project life cycle. Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects. Based on this experience, we doubt that prior work on generalizing quality models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are available here: https://github.com/snaraya7/early-bird

N. C. Shrikanth, Tim Menzies

Just because software developers say they believe in "X", that does not necessarily mean that "X" is true. As shown here, there exist numerous beliefs listed in the recent Software Engineering literature which are only supported by small portions of the available data. Hence we ask what is the source of this disconnect between beliefs and evidence?. To answer this question we look for evidence for ten beliefs within 300,000+ changes seen in dozens of open-source projects. Some of those beliefs had strong support across all the projects; specifically, "A commit that involves more added and removed lines is more bug-prone" and "Files with fewer lines contributed by their owners (who contribute most changes) are bug-prone". Most of the widely-held beliefs studied are only sporadically supported in the data; i.e. large effects can appear in project data and then disappear in subsequent releases. Such sporadic support explains why developers believe things that were relevant to their prior work, but not necessarily their current work. Our conclusion will be that we need to change the nature of the debate with Software Engineering. Specifically, while it is important to report the effects that hold right now, it is also important to report on what effects change over time.

N. C. Shrikanth, Suvodeep Majumder, Tim Menzies

Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate this, we analyzed hundreds of popular GitHub projects. These projects ran for 84 months and contained 3,728 commits (median values). Across these projects, most of the defects occur very early in their life cycle. Hence, defect predictors learned from the first 150 commits and four months perform just as well as anything else. This means that, at least for the projects studied here, after the first few months, we need not continually update our defect prediction models. We hope these results inspire other researchers to adopt a "simplicity-first" approach to their work. Some domains require a complex and data-hungry analysis. But before assuming complexity, it is prudent to check the raw data looking for "short cuts" that can simplify the analysis.

N. C. Shrikanth, William Nichols, Fahmid Morshed Fahid et al.

Software engineering is a highly dynamic discipline. Hence, as times change, so too might our beliefs about core processes in this field. This paper checks some five beliefs that originated in the past decades that comment on the relationships between (i) developer productivity; (ii) software quality and (iii) years of developer experience. Using data collected from 1,356 developers in the period 1995 to 2006, we found support for only one of the five beliefs titled "Quality entails productivity". We found no clear support for four other beliefs based on programming languages and software developers. However, from the sporadic evidence of the four other beliefs we learned that a narrow scope could delude practitioners in misinterpreting certain effects to hold in their day to day work. Lastly, through an aggregated view of assessing the five beliefs, we find programming languages act as a confounding factor for developer productivity and software quality. Thus the overall message of this work is that it is both important and possible to revisit old beliefs in SE. Researchers and practitioners should routinely retest old beliefs.