2,687 research outputs found
Proactive Empirical Assessment of New Language Feature Adoption via Automated Refactoring: The Case of Java 8 Default Methods
Programming languages and platforms improve over time, sometimes resulting in
new language features that offer many benefits. However, despite these
benefits, developers may not always be willing to adopt them in their projects
for various reasons. In this paper, we describe an empirical study where we
assess the adoption of a particular new language feature. Studying how
developers use (or do not use) new language features is important in
programming language research and engineering because it gives designers
insight into the usability of the language to create meaning programs in that
language. This knowledge, in turn, can drive future innovations in the area.
Here, we explore Java 8 default methods, which allow interfaces to contain
(instance) method implementations.
Default methods can ease interface evolution, make certain ubiquitous design
patterns redundant, and improve both modularity and maintainability. A focus of
this work is to discover, through a scientific approach and a novel technique,
situations where developers found these constructs useful and where they did
not, and the reasons for each. Although several studies center around assessing
new language features, to the best of our knowledge, this kind of construct has
not been previously considered.
Despite their benefits, we found that developers did not adopt default
methods in all situations. Our study consisted of submitting pull requests
introducing the language feature to 19 real-world, open source Java projects
without altering original program semantics. This novel assessment technique is
proactive in that the adoption was driven by an automatic refactoring approach
rather than waiting for developers to discover and integrate the feature
themselves. In this way, we set forth best practices and patterns of using the
language feature effectively earlier rather than later and are able to possibly
guide (near) future language evolution. We foresee this technique to be useful
in assessing other new language features, design patterns, and other
programming idioms
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An empirical study of evolution of inheritance in Java OSS
Previous studies of Object-Oriented (OO) software have reported avoidance of the inheritance mechanism and cast doubt on the wisdom of ‘deep’ inheritance levels. From an evolutionary perspective, the picture is unclear - we still know relatively little about how, over time, changes tend to be applied by developers. Our conjecture is that an inheritance hierarchy will tend to grow ‘breadth-wise’ rather than ‘depth-wise’. This claim is made on the basis that developers will avoid extending depth in favour of breadth because of the inherent complexity of having to understand the functionality of superclasses. Thus the goal of our study is to investigate this empirically. We conduct an empirical study of seven Java Open-Source Systems (OSSs) over a series of releases to observe the nature and location of changes within the inheritance hierarchies. Results show a strong tendency for classes to be added at levels one and two of the hierarchy (rather than anywhere else). Over 96% of classes added over the course of the versions of all systems were at level 1 or level 2. The results suggest that changes cluster in the shallow levels of a hierarchy; this is relevant for developers since it indicates where remedial activities such as refactoring should be focused
Mutation Testing as a Safety Net for Test Code Refactoring
Refactoring is an activity that improves the internal structure of the code
without altering its external behavior. When performed on the production code,
the tests can be used to verify that the external behavior of the production
code is preserved. However, when the refactoring is performed on test code,
there is no safety net that assures that the external behavior of the test code
is preserved. In this paper, we propose to adopt mutation testing as a means to
verify if the behavior of the test code is preserved after refactoring.
Moreover, we also show how this approach can be used to identify the part of
the test code which is improperly refactored
Technical Debt Prioritization: State of the Art. A Systematic Literature Review
Background. Software companies need to manage and refactor Technical Debt
issues. Therefore, it is necessary to understand if and when refactoring
Technical Debt should be prioritized with respect to developing features or
fixing bugs. Objective. The goal of this study is to investigate the existing
body of knowledge in software engineering to understand what Technical Debt
prioritization approaches have been proposed in research and industry. Method.
We conducted a Systematic Literature Review among 384 unique papers published
until 2018, following a consolidated methodology applied in Software
Engineering. We included 38 primary studies. Results. Different approaches have
been proposed for Technical Debt prioritization, all having different goals and
optimizing on different criteria. The proposed measures capture only a small
part of the plethora of factors used to prioritize Technical Debt qualitatively
in practice. We report an impact map of such factors. However, there is a lack
of empirical and validated set of tools. Conclusion. We observed that technical
Debt prioritization research is preliminary and there is no consensus on what
are the important factors and how to measure them. Consequently, we cannot
consider current research conclusive and in this paper, we outline different
directions for necessary future investigations
What to Fix? Distinguishing between design and non-design rules in automated tools
Technical debt---design shortcuts taken to optimize for delivery speed---is a
critical part of long-term software costs. Consequently, automatically
detecting technical debt is a high priority for software practitioners.
Software quality tool vendors have responded to this need by positioning their
tools to detect and manage technical debt. While these tools bundle a number of
rules, it is hard for users to understand which rules identify design issues,
as opposed to syntactic quality. This is important, since previous studies have
revealed the most significant technical debt is related to design issues. Other
research has focused on comparing these tools on open source projects, but
these comparisons have not looked at whether the rules were relevant to design.
We conducted an empirical study using a structured categorization approach, and
manually classify 466 software quality rules from three industry tools---CAST,
SonarQube, and NDepend. We found that most of these rules were easily labeled
as either not design (55%) or design (19%). The remainder (26%) resulted in
disagreements among the labelers. Our results are a first step in formalizing a
definition of a design rule, in order to support automatic detection.Comment: Long version of accepted short paper at International Conference on
Software Architecture 2017 (Gothenburg, SE
Organizing the Technical Debt Landscape
To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subjec
A Longitudinal Study of Identifying and Paying Down Architectural Debt
Architectural debt is a form of technical debt that derives from the gap
between the architectural design of the system as it "should be" compared to
"as it is". We measured architecture debt in two ways: 1) in terms of
system-wide coupling measures, and 2) in terms of the number and severity of
architectural flaws. In recent work it was shown that the amount of
architectural debt has a huge impact on software maintainability and evolution.
Consequently, detecting and reducing the debt is expected to make software more
amenable to change. This paper reports on a longitudinal study of a healthcare
communications product created by Brightsquid Secure Communications Corp. This
start-up company is facing the typical trade-off problem of desiring
responsiveness to change requests, but wanting to avoid the ever-increasing
effort that the accumulation of quick-and-dirty changes eventually incurs. In
the first stage of the study, we analyzed the status of the "before" system,
which indicated the impacts of change requests. This initial study motivated a
more in-depth analysis of architectural debt. The results of this analysis were
used to motivate a comprehensive refactoring of the software system. The third
phase of the study was a follow-on architectural debt analysis which quantified
the improvements made. Using this quantitative evidence, augmented by
qualitative evidence gathered from in-depth interviews with Brightsquid's
architects, we present lessons learned about the costs and benefits of paying
down architecture debt in practice.Comment: Submitted to ICSE-SEIP 201
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