214 research outputs found
10161 Abstracts Collection -- Decision Procedures in Software, Hardware and Bioware
From April 19th, 2010 to April 23rd, 2010, the Dagstuhl Seminar 10161
"Decision Procedures in Soft, Hard and Bio-ware"
was held in Schloss Dagstuhl Leibniz Center for Informatics.
During the seminar, several participants presented their current research,
and ongoing work and open problems were discussed. Abstracts of the
presentations given during the seminar as well as links to slides and links to
papers behind the presentations and papers produced as a result
of the seminar are put together in this paper. The first section describes
the seminar topics and goals in general. Links to extended abstracts or
full papers are provided, if available
Threats to the validity of mutation-based test assessment
Much research on software testing and test techniques relies on experimental studies based on mutation testing. In this paper we reveal that such studies are vulnerable to a potential threat to validity, leading to possible Type I errors; incorrectly rejecting the Null Hypothesis. Our findings indicate that Type I errors occur, for arbitrary experiments that fail to take countermeasures, approximately 62% of the time. Clearly, a Type I error would potentially compromise any scientific conclusion. We show that the problem derives from such studies’ combined use of both subsuming and subsumed mutants. We collected articles published in the last two years at three leading software engineering conferences. Of those that use mutation-based test assessment, we found that 68% are vulnerable to this threat to validity
FOREPOST: A Tool For Detecting Performance Problems with Feedback-Driven Learning Software Testing
A goal of performance testing is to find situations when applications unexpectedly exhibit worsened characteristics for certain combinations of input values. A fundamental question of performance testing is how to select a manageable subset of the input data faster to find performance problems in applications automatically. We present a novel tool, FOREPOST, for finding performance problems in applications automatically using black-box software testing. In this paper, we demonstrate how FOREPOST extracts rules from execution traces of applications by using machine learning algorithms, and then uses these rules to select test input data automatically to steer applications towards computationally intensive paths and to find performance problems
Putting the Semantics into Semantic Versioning
The long-standing aspiration for software reuse has made astonishing strides
in the past few years. Many modern software development ecosystems now come
with rich sets of publicly-available components contributed by the community.
Downstream developers can leverage these upstream components, boosting their
productivity.
However, components evolve at their own pace. This imposes obligations on and
yields benefits for downstream developers, especially since changes can be
breaking, requiring additional downstream work to adapt to. Upgrading too late
leaves downstream vulnerable to security issues and missing out on useful
improvements; upgrading too early results in excess work. Semantic versioning
has been proposed as an elegant mechanism to communicate levels of
compatibility, enabling downstream developers to automate dependency upgrades.
While it is questionable whether a version number can adequately characterize
version compatibility in general, we argue that developers would greatly
benefit from tools such as semantic version calculators to help them upgrade
safely. The time is now for the research community to develop such tools: large
component ecosystems exist and are accessible, component interactions have
become observable through automated builds, and recent advances in program
analysis make the development of relevant tools feasible. In particular,
contracts (both traditional and lightweight) are a promising input to semantic
versioning calculators, which can suggest whether an upgrade is likely to be
safe.Comment: to be published as Onward! Essays 202
Sanitizing and Minimizing Databases for Software Application Test Outsourcing
Abstract—Testing software applications that use nontrivial databases is increasingly outsourced to test centers in order to achieve lower cost and higher quality. Not only do different data privacy laws prevent organizations from sharing this data with test centers because databases contain sensitive information, but also this situation is aggravated by big data – it is time consum-ing and difficult to anonymize, distribute, and test with large databases. Deleting data randomly often leads to significantly worsened test coverages and fewer uncovered faults, thereby reducing the quality of software applications. We propose a novel approach for Protecting and mInimizing databases for Software TestIng taSks (PISTIS) that both sanitizes and minimizes a database that comes along with an application. PISTIS uses a weight-based data clustering algorithm that partitions data in the database using information obtained using program analysis that describes how this data is used by the application. For each cluster, a centroid object is computed that represents different persons or entities in the cluster, and we use associative rule mining to compute and use constraints to ensure that the centroid objects are representative of the general population of the data in the cluster. Doing so also sanitizes information, since these centroid objects replace the original data to make it difficult for attackers to infer sensitive information. Thus, we reduce a large database to a few centroid objects and we show in our experiments with two applications that test coverage stays within a close range to its original level. I
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