160,558 research outputs found
Software Measurement Activities in Small and Medium Enterprises: an Empirical Assessment
An empirical study for evaluating the proper implementation of measurement/metric programs in software companies in one area of Turkey is presented. The research questions are discussed and validated with the help of senior software
managers (more than 15 yearsâ experience) and then used for interviewing a variety of medium and small scale software companies in Ankara. Observations show that there is a
common reluctance/lack of interest in utilizing measurements/metrics despite the fact that they are well known in the industry. A side product of this research is that internationally recognized standards such as ISO and CMMI are pursued if they are a part of project/job
requirements; without these requirements, introducing those standards to the companies remains as a long-term target to increase quality
Structured Review of the Evidence for Effects of Code Duplication on Software Quality
This report presents the detailed steps and results of a structured review of code clone literature. The aim of the review is to investigate the evidence for the claim that code duplication has a negative effect on code changeability. This report contains only the details of the review for which there is not enough place to include them in the companion paper published at a conference (Hordijk, Ponisio et al. 2009 - Harmfulness of Code Duplication - A Structured Review of the Evidence)
An Approach for the Empirical Validation of Software Complexity Measures
Software metrics are widely accepted tools to control and assure software quality. A large number of software metrics with a variety of content can be found in the literature; however most of them are not adopted in industry as they are seen as irrelevant to needs, as they are unsupported, and the major reason behind this is due to improper
empirical validation. This paper tries to identify possible root causes for the improper empirical validation of the software metrics. A practical model for the empirical validation of software metrics is proposed along with root causes. The model is validated by applying it to recently proposed and well known metrics
Evaluation Criteria for Object-oriented Metrics
In this paper an evaluation model for object-oriented (OO) metrics is proposed. We have evaluated the existing evaluation criteria for OO metrics, and based on the observations, a model is proposed which tries to cover most of the features for the evaluation of OO metrics. The model is validated by applying it to existing OO metrics. In contrast to the other existing criteria, the proposed model is simple in implementation and includes the practical and important aspects of evaluation; hence it suitable to evaluate and validate any OO complexity metric
Trustworthy Experimentation Under Telemetry Loss
Failure to accurately measure the outcomes of an experiment can lead to bias
and incorrect conclusions. Online controlled experiments (aka AB tests) are
increasingly being used to make decisions to improve websites as well as mobile
and desktop applications. We argue that loss of telemetry data (during upload
or post-processing) can skew the results of experiments, leading to loss of
statistical power and inaccurate or erroneous conclusions. By systematically
investigating the causes of telemetry loss, we argue that it is not practical
to entirely eliminate it. Consequently, experimentation systems need to be
robust to its effects. Furthermore, we note that it is nontrivial to measure
the absolute level of telemetry loss in an experimentation system. In this
paper, we take a top-down approach towards solving this problem. We motivate
the impact of loss qualitatively using experiments in real applications
deployed at scale, and formalize the problem by presenting a theoretical
breakdown of the bias introduced by loss. Based on this foundation, we present
a general framework for quantitatively evaluating the impact of telemetry loss,
and present two solutions to measure the absolute levels of loss. This
framework is used by well-known applications at Microsoft, with millions of
users and billions of sessions. These general principles can be adopted by any
application to improve the overall trustworthiness of experimentation and
data-driven decision making.Comment: Proceedings of the 27th ACM International Conference on Information
and Knowledge Management, October 201
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