Suggested scenarios of software maintenance education

2005 ◽  
pp. 329-340 ◽  
Author(s):  
Frank W. Calliss ◽  
Debra Trantina Calliss
Keyword(s):  
Software Maintenance

scholarly journals Making the Sourcing Decision of Software Maintenance and Information Technology

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 11492-11510
Author(s):  
Hanif Ur Rahman ◽  
Mushtaq Raza ◽  
Palwasha Afsar ◽  
Mukhtaj Khan ◽  
Nadeem Iqbal ◽  
...  
Keyword(s):  
Information Technology ◽  
Software Maintenance

scholarly journals Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics

Technologies ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Gábor Antal ◽  
Zoltán Tóth ◽  
Péter Hegedűs ◽  
Rudolf Ferenc
Keyword(s):  
Software Maintenance ◽  
Positive Impact ◽  
Source Code ◽  
Code Analysis ◽  
Static Source ◽  
Static Code Analysis ◽  
Function Calls ◽  
Hybrid Code ◽  
Code Metrics ◽  
Scripting Language

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.

Optimal Software Release Policies with Debugging Time Lag

1997 ◽  
Vol 04 (03) ◽  
pp. 241-255 ◽  
Author(s):  
Tadashi Dohi ◽  
Naoto Kaio ◽  
Shunji Osaki
Keyword(s):  
Error Detection ◽  
Software Maintenance ◽  
Growth Models ◽  
Time Lag ◽  
Computer Software ◽  
Release Time ◽  
Software Release ◽  
Software Reliability Growth ◽  
Operation Phase ◽  
Non Homogeneous Poisson Process

This paper presents a new stochastic model for determining the optimal release time for a computer software in testing phase, taking account of the debugging time lag. In the earlier works, most of software release models were considered, but it was assumed that an error detected can be removed instantaneously. In other words, none discussed quantitatively the effect of the software maintenance action in the optimal software release time. Main purpose of this work is to relate the optimal software release policy with the arrival-service process on the software operation phase by users. We use the Non-Homogeneous Poisson Process (NHPP) type of software reliability growth models as the software error detection phenomena and obtain the optimal software release policies minimizing the expected total software costs. As a result, the usage circumstance of a software in operation phase gives a monotone effect to the software release planning.

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