178 research outputs found
Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)
We report and fix an important systematic error in prior studies that ranked
classifiers for software analytics. Those studies did not (a) assess
classifiers on multiple criteria and they did not (b) study how variations in
the data affect the results. Hence, this paper applies (a) multi-criteria tests
while (b) fixing the weaker regions of the training data (using SMOTUNED, which
is a self-tuning version of SMOTE). This approach leads to dramatically large
increases in software defect predictions. When applied in a 5*5
cross-validation study for 3,681 JAVA classes (containing over a million lines
of code) from open source systems, SMOTUNED increased AUC and recall by 60% and
20% respectively. These improvements are independent of the classifier used to
predict for quality. Same kind of pattern (improvement) was observed when a
comparative analysis of SMOTE and SMOTUNED was done against the most recent
class imbalance technique. In conclusion, for software analytic tasks like
defect prediction, (1) data pre-processing can be more important than
classifier choice, (2) ranking studies are incomplete without such
pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.Comment: 10 pages + 2 references. Accepted to International Conference of
Software Engineering (ICSE), 201
Revisiting the conclusion instability issue in software effort estimation
Conclusion instability is the absence of observing the same effect under varying experimental conditions. Deep Neural Network (DNN) and ElasticNet software effort estimation (SEE) models were applied to two SEE datasets with the view of resolving the conclusion instability issue and assessing the suitability of ElasticNet as a viable SEE benchmark model. Results were mixed as both model types attain conclusion stability for the Kitchenham dataset whilst conclusion instability existed in the Desharnais dataset. ElasticNet was outperformed by DNN and as such it is not recommended to be used as a SEE benchmark model
Further Investigation of the Survivability of Code Technical Debt Items
Context: Technical Debt (TD) discusses the negative impact of sub-optimal
decisions to cope with the need-for-speed in software development. Code
Technical Debt Items (TDI) are atomic elements of TD that can be observed in
code artefacts. Empirical results on open-source systems demonstrated how
code-smells, which are just one type of TDIs, are introduced and "survive"
during release cycles. However, little is known about whether the results on
the survivability of code-smells hold for other types of code TDIs (i.e., bugs
and vulnerabilities) and in industrial settings. Goal: Understanding the
survivability of code TDIs by conducting an empirical study analysing two
industrial cases and 31 open-source systems from Apache Foundation. Method: We
analysed 133,670 code TDIs (35,703 from the industrial systems) detected by
SonarQube (in 193,196 commits) to assess their survivability using
survivability models. Results: In general, code TDIs tend to remain and linger
for long periods in open-source systems, whereas they are removed faster in
industrial systems. Code TDIs that survive over a certain threshold tend to
remain much longer, which confirms previous results. Our results also suggest
that bugs tend to be removed faster, while code smells and vulnerabilities tend
to survive longer.Comment: Submitted to the Journal of Software: Evolution and Process (JSME
Rework Effort Estimation of Self-admitted Technical Debt
Programmers sometimes leave incomplete, temporary workarounds and buggy codes that require rework. This phenomenon in software development is referred to as Self- admitted Technical Debt (SATD). The challenge therefore is for software engineering researchers and practitioners to resolve the SATD problem to improve the software quality. We performed an exploratory study using a text mining approach to extract SATD from developers’ source code comments and implement an effort metric to compute the rework effort that might be needed to resolve the SATD problem. The result of this study confirms the result of a prior study that found design debt to be the most predominant class of SATD. Results from this study also indicate that a significant amount of rework effort of between 13 and 32 commented LOC on average per SATD prone source file is required to resolve the SATD challenge across all the four projects considered. The text mining approach incorporated into the rework effort metric will speed up the extraction and analysis of SATD that are generated during software projects. It will also aid in managerial decisions of whether to handle SATD as part of on-going project development or defer it to the maintenance phase
Multi-Objective Optimization for Software Testing Effort Estimation
Software Testing Effort (STE), which contributes about 25-40% of the total development effort, plays a significant role in software development. In addressing the issues faced by companies in finding relevant datasets for STE estimation modeling prior to development, cross-company modeling could be leveraged. The study aims at assessing the effectiveness of cross-company (CC) and within-company (WC) projects in STE estimation. A robust multi-objective Mixed-Integer Linear Programming (MILP) optimization framework for the selection of CC and WC projects was constructed and estimation of STE was done using Deep Neural Networks. Results from our study indicate that the application of the MILP framework yielded similar results for both WC and CC modeling. The modeling framework will serve as a foundation to assist in STE estimation prior to the development of new a software project
A systematic review on food recommender systems
The Internet has revolutionised the way information is retrieved, and the increase in the number of users has resulted in a surge in the volume and heterogeneity of available data. Recommender systems have become popular tools to help users retrieve relevant information quickly. Food Recommender Systems (FRS), in particular, have proven useful in overcoming the overload of information present in the food domain. However, the recommendation of food is a complex domain with specific characteristics causing many challenges. Additionally, very few systematic literature reviews have been conducted in the domain on FRS. This paper presents a systematic literature review that summarises the current state-of-the-art in FRS. Our systematic review examines the different methods and algorithms used for recommendation, the data and how it is processed, and evaluation methods. It also presents the advantages and disadvantages of FRS. To achieve this, a total of 67 high-quality studies were selected from a pool of 2,738 studies using strict quality criteria. The review reveals that the domain of food recommendation is very diverse, and most FRS are built using content-based filtering and ML approaches to provide non-personalised recommendations. The review provides valuable information to the research field, helping researchers in the domain to select a strategy to develop FRS. This review can help improve the efficiency of development, thus closing the gap between the development of FRS and other recommender systems.</p
Keeping Products of Higher Educational Institutions (HEIs) of Relevance to the Industry: A Reason to Stay in Touch with Alumni
This paper aims to show how important it is for Higher Educational Institutions (HEIs) to be involved in the Continuous Professional Development (CPD) of alumni. The authors provide a model to show the value added to the product by CPD. This paper proposes ways in which institutions can make the best of the relationship with the student at each level proposed in the model. The major findings were that HEIs should get involved in CPD for its Alumni and CPD provides increased value to products of HEIs. Implications were that HEIs have a role to play in the CPD of their Alumni. This is the first study to introduce the Value- Adding CPD Knowledge Cycle Model. Keywords: HEI, Products, CPD, Students, Graduates, Alumn
Does class size matter? An in-depth assessment of the effect of class size in software defect prediction
In the past 20 years, defect prediction studies have generally acknowledged
the effect of class size on software prediction performance. To quantify the
relationship between object-oriented (OO) metrics and defects, modelling has to
take into account the direct, and potentially indirect, effects of class size
on defects. However, some studies have shown that size cannot be simply
controlled or ignored, when building prediction models. As such, there remains
a question whether, and when, to control for class size. This study provides a
new in-depth examination of the impact of class size on the relationship
between OO metrics and software defects or defect-proneness. We assess the
impact of class size on the number of defects and defect-proneness in software
systems by employing a regression-based mediation (with bootstrapping) and
moderation analysis to investigate the direct and indirect effect of class size
in count and binary defect prediction. Our results show that the size effect is
not always significant for all metrics. Of the seven OO metrics we
investigated, size consistently has significant mediation impact only on the
relationship between Coupling Between Objects (CBO) and
defects/defect-proneness, and a potential moderation impact on the relationship
between Fan-out and defects/defect-proneness. Based on our results we make
three recommendations. One, we encourage researchers and practitioners to
examine the impact of class size for the specific data they have in hand and
through the use of the proposed statistical mediation/moderation procedures.
Two, we encourage empirical studies to investigate the indirect effect of
possible additional variables in their models when relevant. Three, the
statistical procedures adopted in this study could be used in other empirical
software engineering research to investigate the influence of potential
mediators/moderators.Comment: Accepted to Empirical Software Engineering (to appear). arXiv admin
note: text overlap with arXiv:2104.1234
A systematic review on food recommender systems
The Internet has revolutionised the way information is retrieved, and the increase in the number of users has resulted in a surge in the volume and heterogeneity of available data. Recommender systems have become popular tools to help users retrieve relevant information quickly. Food Recommender Systems (FRS), in particular, have proven useful in overcoming the overload of information present in the food domain. However, the recommendation of food is a complex domain with specific characteristics causing many challenges. Additionally, very few systematic literature reviews have been conducted in the domain on FRS. This paper presents a systematic literature review that summarises the current state-of-the-art in FRS. Our systematic review examines the different methods and algorithms used for recommendation, the data and how it is processed, and evaluation methods. It also presents the advantages and disadvantages of FRS. To achieve this, a total of 67 high-quality studies were selected from a pool of 2,738 studies using strict quality criteria. The review reveals that the domain of food recommendation is very diverse, and most FRS are built using content-based filtering and ML approaches to provide non-personalised recommendations. The review provides valuable information to the research field, helping researchers in the domain to select a strategy to develop FRS. This review can help improve the efficiency of development, thus closing the gap between the development of FRS and other recommender systems.</p
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