440 research outputs found
Lightweight Assessment of Test-Case Effectiveness using Source-Code-Quality Indicators
No full textTest cases are crucial to help developers preventing the introduction of software faults. Unfortunately, not all the tests are properly designed or can effectively capture faults in production code. Some measures have been defined to assess test-case effectiveness: the most relevant one is the mutation score, which highlights the quality of a test by generating the so-called mutants, ie variations of the production code that make it faulty and that the test is supposed to identify. However, previous studies revealed that mutation analysis is extremely costly and hard to use in practice. The approaches proposed by researchers so far have not been able to provide practical gains in terms of mutation testing efficiency. This leaves the problem of efficiently assessing test-case effectiveness as still open. In this paper, we investigate a novel, orthogonal, and lightweight methodology to assess test-case effectiveness: in particular, we study the feasibility to exploit production and test-code-quality indicators to estimate the mutation score of a test case. We firstly select a set of 67 factors and study their relation with test-case effectiveness. Then, we devise a mutation score estimation model exploiting such factors and investigate its performance as well as its most relevant features. The key results of the study reveal that our estimation model only based on static features has 86% of both F-Measure and AUC-ROC. This means that we can estimate the test-case effectiveness, using source-code-quality indicators, with high accuracy and without executing the tests. As a consequence, we can provide a practical approach that is beyond the typical limitations of current mutation testing techniques
How Developers Engage with Static Analysis Tools in Different Contexts
Full text linkAutomatic static analysis tools (ASATs) are instruments that support code quality assessment by automatically detecting defects and design issues. Despite their popularity, they are characterized by (i) a high false positive rate and (ii) the low comprehensibility of the generated warnings. However, no prior studies have investigated the usage of ASATs in different development contexts (e.g., code reviews, regular development), nor how open source projects integrate ASATs into their workflows. These perspectives are paramount to improve the prioritization of the identified warnings. To shed light on the actual ASATs usage practices, in this paper we first survey 56 developers (66% from industry and 34% from open source projects) and interview 11 industrial experts leveraging ASATs in their workflow with the aim of understanding how they use ASATs in different contexts. Furthermore, to investigate how ASATs are being used in the workflows of open source projects, we manually inspect the contribution guidelines of 176 open-source systems and extract the ASATs’ configuration and build files from their corresponding GitHub repositories. Our study highlights that (i) 71% of developers do pay attention to different warning categories depending on the development context; (ii) 63% of our respondents rely on specific factors (e.g., team policies and composition) when prioritizing warnings to fix during their programming; and (iii) 66% of the projects define how to use specific ASATs, but only 37% enforce their usage for new contributions. The perceived relevance of ASATs varies between different projects and domains, which is a sign that ASATs use is still not a common practice. In conclusion, this study confirms previous findings on the unwillingness of developers to configure ASATs and it emphasizes the necessity to improve existing strategies for the selection and prioritization of ASATs warnings that are shown to developers
Ocelot: A search-based test-data generation tool for C
Full text linkAutomatically generating test cases plays an important role to reduce the time spent by developers during the testing phase. In last years, several approaches have been proposed to tackle such a problem: amongst others, search-based techniques have been shown to be particularly promising. In this paper we describe Ocelot, a search-based tool for the automatic generation of test cases in C. Ocelot allows practitioners to write skeletons of test cases for their programs and researchers to easily implement and experiment new approaches for automatic test-data generation. We show that Ocelot achieves a higher coverage compared to a competitive tool in 81% of the cases. Ocelot is publicly available to support both researchers and practitioners
How can I improve my app? Classifying user reviews for software maintenance and evolution
Full text linkApp Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%)
Exploiting Natural Language Structures in Software Informal Documentation
No full textCommunication means, such as issue trackers, mailing lists, QA forums, and app reviews, are premier means of collaboration among developers, and between developers and end-users. Analyzing such sources of information is crucial to build recommenders for developers, for example suggesting experts, re-documenting source code, or transforming user feedback in maintenance and evolution strategies for developers. To ease this analysis, in previous work we proposed Development Emails Content Analyzer (DECA), a tool based on Natural Language Parsing that classifies with high precision development emails' fragments according to their purpose. However, DECA has to be trained through a manual tagging of relevant patterns, which is often effort-intensive, error-prone and requires specific expertise in natural language parsing. In this paper, we first show, with an empirical study, the extent to which producing rules for identifying such patterns requires effort, depending on the nature and complexity of patterns. Then, we propose an approach, named Nlp-based softwarE dOcumentation aNalyzer (NEON), that automatically mines such rules, minimizing the manual effort. We assess the performances of NEON in the analysis and classification of mobile app reviews, developers discussions, and issues. NEON simplifies the patterns identification and rules definition processes, allowing a savings of more than 70 percent of the time otherwise spent on performing such activities manually. Results also show that NEON-generated rules are close to the manually identified ones, achieving comparable recall
ARdoc: App Reviews Development Oriented Classifier
Full text linkGoogle Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality. In this demo we present ARdoc, a tool which combines three techniques: (1) Natural Language Parsing (NLP), (2) Text Analysis (TA) and (3) Sentiment Analysis (SA) to automatically classify useful feedback contained in
app reviews important for performing software maintenance and evolution tasks. Our quantitative and qualitative analysis (involving mobile professional developers) demonstrate that ARdoc correctly classi�es feedback useful for maintenance perspectives in user reviews with high precision (ranging between 84% and 89%), recall (ranging between 84% and 89%), and an F-Measure (ranging between 84% and 89%).
While evaluating our tool we also found that ARdoc substantially helps to extract important maintenance tasks for real world applications.
Demo URL: https://youtu.be/Baf18V6sN8E
Demo Web Page: http://www.ifi.uzh.ch/seal/people/panichella/tools/ARdoc.htm
Paul Grüninger Collection ; 1984, 1996
No full textLetters written in response to articles on Paul Grüninger.‚Aktenzeichen Grüninger – ungelöst?‘ by Heinz Roschewski,Die Güninger Legende‘ by Harald HuberProcessed for digitizationPaul Grüninger was a Swiss police captain in Saint Gall who aided in the flight of several thousands of Jewish refugees from the German Nazi government. He was terminated from his position, possibly as a result of his rescue efforts.digitizedLeib, Otto S. ; Sandor, Lancelot C. ; Mayer, Saly, 1882-195
Towards safer composition
Full text linkDetermining whether a set of features can be composed, or safe composition, is a hard problem in software product line engineering because the number of feature combinations can be exponential. We argue that synergies between current approaches to safe composition should be exploited and propose a combined approach. At the heart of our proposal is a merge operation that creates a behavioural description for the entire product family from a feature diagram and descriptions of individual feature behaviour. As a result, we intend to verify more efficiently safe composition for an exponential number of feature combinations
- …
